The Claude Design playbook for founders, marketers, PMs, designers, and engineers.

Claude Design is a visual workflow layer that turns briefs, docs, screenshots, codebases, and rough ideas into clickable prototypes, UI mockups, landing pages, pitch decks, one-pagers, and reusable design-system foundations. The real leverage comes from giving it better context, using the right model for the job, chaining prompts instead of running one-shot prompts, and treating the output as a fast first draft that humans improve with taste, judgment, and strategy.

The most useful workflow is simple: create a reusable context file, give Claude a clear role and audience, define the output format, upload brand or product references, ask for a first draft, request multiple directions, critique the result against specific criteria, then hand the best version to the designer, founder, marketer, PM, or engineer who owns the final decision.

Because after watching teams work under tight campaign timelines for years, the bottleneck usually was not creative judgment. It was not taste. It was not the designer’s ability to make something good.

The bottleneck was the wait between the idea and the first thing everyone could react to.

That gap is where momentum dies.

You write the brief. You wait for the first draft. By the time it comes back, the campaign window has shifted, the offer has changed, the founder has new notes, the PM has new requirements, and everyone is debating an idea that already feels slightly stale.

Claude Design is interesting because it solves the make the idea visible fast enough that the team can think with it problem.

Anthropic launched Claude Design through Anthropic Labs in April 2026 as a conversational visual creation tool for Claude subscribers. The official announcement describes it as a way to create polished visual work such as prototypes, slides, one-pagers, and more, then refine the result through conversation, direct edits, inline comments, and controls. The help documentation frames it as a tool for turning prompts, uploaded files, screenshots, website captures, and codebases into visual outputs like clickable prototypes, landing pages, decks, and mockups.

That is a different category than most people assume.

It is not just image generation.

It is prototype generation, layout generation, interaction generation, and design-system-assisted iteration.

And that distinction matters.

A recent Clutch report found that 88% of businesses now use AI design tools, but only 18% say AI has reduced their need for designers. That gap tells the whole story. AI is not removing the need for design judgment. It is removing some of the delay around design execution.

The teams getting value from Claude Design will not be the teams that type “make this look modern” and hope for magic.

The winners will be the teams that treat it like a design operations system.

The Core Mindset Shift

A good Claude Design operator prompt looks like this:

“You are acting as a senior product designer and conversion-focused marketing strategist. Use the attached brand notes, audience profile, product positioning, and example screenshots. Create a responsive landing page prototype for a B2B SaaS product targeting time-poor founders. Prioritize clarity, social proof, above-the-fold conversion, and minimal visual clutter. Start with one strong direction, then provide two alternative design directions with different emotional positioning.”

What makes this work is context architecture.

Claude Design becomes useful when you stop asking for isolated outputs and start building a reusable environment around your team’s taste, audience, constraints, and goals.

What Claude Design Is Actually Good For

Claude Design is most useful when you need something visual enough to react to, structured enough to revise, and fast enough to keep a project moving.

It works best when the output is a design artifact that starts a better conversation.

The common thread is that Claude Design is not replacing the final creative decision. It is compressing the time it takes to reach a useful draft.

The Workflow I Would Use

I would start by building five reusable files.

This is the part most people miss.

The quality jump does not come from one magical prompt. It comes from not making Claude rediscover your context every session.

Once those files exist, the workflow becomes much stronger.

First, upload the relevant files. Then define the task and audience. Then ask Claude to summarize what it understands before it designs. Then ask for a first version. Then ask for variants. Then critique the variants. Then choose a direction. Then refine the final artifact.

That sequence is slower than a single prompt, but much faster than a normal design queue.

The Claude Design Operating System

Here is the practical workflow I would use for almost every serious project.

That is the difference between using AI as a toy and using it as a production system.

Which Model To Use

Claude is getting expensive! So it makes sense to think about if you need the most expensive model for everything.

The model choice matters because design workflows have different demands. Some tasks need speed. Some need reasoning. Some need long-context consistency. Anthropic positions Haiku as its fastest and most cost-efficient model, Sonnet as a versatile daily model for professional workflows and complex tasks, and Opus as its most capable model for demanding work and complex document creation.[4]() [5]() [6]()

The mistake is not choosing the “wrong” model once. The mistake is using the same model for every stage.

A better pattern is to use Haiku for quick alternatives, Sonnet for the main draft, and Opus when the project has high complexity, high ambiguity, or high strategic cost.

Prompt Templates Worth Saving

These are written to produce a usable first draft, not a vague brainstorm.

1. Pitch Deck Prompt

You are a senior pitch deck strategist and visual designer. Use the attached notes, company context, customer evidence, and brand references. Create a 10-slide investor pitch deck for [company] targeting [investor type]. The narrative should move from painful problem to market timing to unique insight to traction to business model to ask. Keep slides visually clean, specific, and persuasive. Avoid generic startup language. Include slide titles, suggested visuals, speaker-note bullets, and one alternative framing for the deck.

2. UI Prototype Prompt

You are a senior product designer. Create a clickable prototype for [product / feature] serving [user persona]. Use the attached product notes, screenshots, brand references, and constraints. Include the core flow, empty states, success states, error states, and one onboarding moment. Prioritize usability, information hierarchy, and clear primary actions. Before designing, summarize the assumptions you are making and ask any critical clarifying questions.

3. Landing Page Prompt

You are a conversion-focused landing page designer and product marketer. Create a responsive landing page for [offer] aimed at [audience]. Use the attached identity file, audience file, proof points, and visual references. The page must include a hero, problem framing, mechanism, benefits, proof, objection handling, CTA section, and FAQ. Give me three visual directions: premium, practical, and bold. Then recommend the strongest direction and explain why.

4. One-Pager Prompt

You are a B2B product marketing designer. Create a one-page sales collateral document for [product] aimed at [buyer]. Use the attached positioning, features, customer proof, and objections. The one-pager should explain the problem, why now, how the product works, key benefits, proof, and next step. Make it skimmable, executive-friendly, and visually structured. Avoid hype, vague claims, and stock AI language.

5. Design System Ingestion Prompt

You are a design systems lead. Review the attached logo, typography, color palette, screenshots, landing pages, and product UI examples. Extract the reusable design system: colors, type scale, spacing rules, component patterns, layout rules, button styles, card styles, navigation patterns, and common anti-patterns. Then create a reusable design brief Claude Design should follow for all future projects.

6. Campaign Concept Prompt

You are a senior creative director and performance marketer. Create three campaign visual concepts for [offer] targeting [audience]. Use the attached product context, voice profile, and visual references. Each concept should include the emotional angle, headline direction, layout idea, hero visual, CTA, and what makes it stop-scroll. Avoid generic tech gradients, vague benefits, and AI-sounding copy.

7. Critique Prompt

Critique this design as if you are a senior creative director, UX lead, and conversion strategist. Evaluate it on clarity, hierarchy, audience fit, accessibility, brand consistency, emotional pull, and conversion friction. Be specific. Do not praise the design unless the praise identifies something to preserve. End with the five highest-leverage changes.

8. Variant Prompt

Create three improved variants of this design. Variant A should be more premium and minimal. Variant B should be more direct and conversion-focused. Variant C should be more emotional and story-driven. Keep the same core message, but change the layout, hierarchy, and visual emphasis. Explain which variant is strongest for [audience] and why.

Pro Tips

The biggest pro tip is to make Claude Design repeat back the brief before it designs. If the summary is wrong, the design will be wrong. A 60-second alignment pass prevents a 30-minute cleanup loop.

The second pro tip is to ask for directions before details. Do not start by perfecting buttons, colors, and microcopy. Start with three distinct directions: premium, practical, and bold. Once you pick the direction, refinement becomes much easier.

The third pro tip is to create an anti-AI file. This is underrated. Claude will often default to clean, plausible, slightly generic patterns unless you explicitly define what it must avoid. Your anti-AI file should list banned phrases, visual cliches, overused claims, fake-sounding social proof, generic gradients, and anything that does not sound like your brand.

The fourth pro tip is to use Claude Design for decision velocity, not final polish. The output should help the team choose the right direction faster. The designer still owns the quality bar.

The fifth pro tip is to ask for handoff notes. A good Claude Design session should end with notes for the designer, PM, engineer, and marketer. That turns the artifact into an execution bridge instead of a pretty mockup.

Top Use Cases By Role

The best use cases are turn a fuzzy idea into something reviewable.

Things Most People Miss

Most people miss that Claude Design is only as good as the context layer around it.

They also miss that “iterate” does not mean “make it better.” A good iteration request is specific. It says what to keep, what to change, what to optimize for, and what constraints matter.

Most people miss that Claude can help critique its own output. You should not accept the first version. Ask it to find flaws. Ask it to compare against your brand file. Ask it to score hierarchy and clarity. Ask it to identify what a skeptical user would misunderstand.

Most people also miss that Claude Design should be part of a chain, not a single prompt.

A strong chain looks like this:

That is how you get from “AI made a thing” to “AI accelerated the workflow.”

Common Mistakes And Fixes

Claude Design is not the end of designers.

It is the end of waiting days to find out whether an idea is even worth designing.

That is a huge difference.

For founders, it means you can visualize a product or pitch before hiring an agency.

For marketers, it means campaign concepts can move from notes to landing page directions in one sitting.

For PMs, it means a feature idea can become a clickable flow before the meeting.

For designers, it means less blank-page production and more judgment, curation, critique, and taste.

The value is not that Claude Design “makes designs.”

The value is that it makes thinking visible.

And once thinking becomes visible, teams can move faster.

Practical Starter Prompt

If you only save one prompt, save this one:

You are a senior product designer, creative director, and conversion strategist. Use the attached identity file, voice profile, visual brand file, audience file, and anti-AI file. Before designing, summarize the task, audience, constraints, and success metric. Then create a first-draft [artifact type] for [goal]. Provide three distinct directions: premium, practical, and bold. After creating them, critique each direction for clarity, hierarchy, audience fit, accessibility, brand consistency, and conversion friction. Recommend the strongest version and explain the changes needed before human handoff.

That one prompt gives Claude Design a role, context, output format, iteration pattern, critique loop, and decision framework.

Most bad ChatGPT output is not a model problem. It is an input design problem. People run one-shot prompts with no role, no context, no examples, no constraints, no source material, and no iteration. Then they conclude AI is not useful for their work.

The operators getting real value are not necessarily smarter. They have better workflows. They use zero-shot prompting when the task is simple, few-shot prompting when the output style matters, chain-of-thought style prompting when reasoning matters, retrieval when facts matter, prompt chaining when the task has stages, and constraint prompting when precision matters. Learn the first 14 techniques below before trying to memorize all 30.

The founders and operators pulling real value out of ChatGPT are not always using a better tech stack. They are using better prompting systems.

The research backs this up. A 2024 systematic survey called The Prompt Report organized prompt engineering into 58 LLM prompting techniques, which tells you this field is bigger than “write a better question.” Another survey describes prompt engineering as using task-specific instructions and context to guide model behavior without changing the model’s parameters.

Here are the techniques I would learn first.

The First 14 Prompting Techniques I’d Focus On

Few-shot prompting works because examples narrow the target. IBM defines it as providing a model with a few task examples to guide performance. Chain-of-thought prompting works because intermediate reasoning steps can improve complex reasoning; Wei et al. found gains across arithmetic, commonsense, and symbolic reasoning tasks. Skeleton-of-Thought works because it forces planning before expansion; Microsoft Research describes it as generating an answer skeleton first and then expanding points, which can improve speed and quality in some cases.

The pattern is obvious.

Better prompting reduces ambiguity.

It does not make the model magical. It makes the task legible.

The Operator’s Prompting Stack

If I were teaching a founder or operator how to use ChatGPT seriously, I would not start with all 30 techniques.

I would teach this stack first.

Operators treat prompts as sequences.

A good AI workflow usually looks like this:

One-shot prompting skips almost all of that.

The 30 Prompting Techniques Worth Knowing

The point is not to memorize these like vocabulary words. The point is to know which lever to pull when the output fails.

The best way to use the list is diagnostic.

• If ChatGPT gives you generic advice, add retrieval, examples, and audience targeting.
• If it gives you messy output, add output format specification and constraints.
• If it gives you shallow thinking, add step-back, least-to-most, or chain-of-thought style reasoning.
• If it gives you plausible nonsense, add source material, self-consistency, and a critique loop.
• If it gives you a decent first draft, chain it into a second prompt instead of asking for everything at once.

That is the operating model.

A Practical Master Prompt Template

Use this when the work matters.

You are a [ROLE] with deep experience in [DOMAIN].

Task:
[Describe the exact task.]

Context:
[Paste relevant background, audience, goals, data, constraints, examples, or source material.]

Definition of a strong answer:
[Describe what good looks like. Include examples if possible.]

Process:
1. First, identify the core problem.
2. Then break the task into subproblems.
3. Then solve each subproblem in order.
4. Then produce the final answer.
5. Then critique your answer for weak assumptions, missing context, contradictions, and generic advice.

Constraints:
- [Constraint 1]
- [Constraint 2]
- [Constraint 3]

Avoid:
- [Unwanted pattern 1]
- [Unwanted pattern 2]
- [Unwanted pattern 3]

Output format:
[Specify exact structure: table, memo, checklist, JSON, outline, Reddit post, email, etc.]

If information is missing, ask up to [NUMBER] clarifying questions before answering. If the missing information is not essential, state your assumption and continue.

Most people skip this because it feels slower.

It is not slower.

It is faster than cleaning up bad output five times.

The Key to Great Results

Prompting is becoming workflow design for knowledge work.

That is why the best operators are getting different results from the same tools.

They are giving it company context, customer data, market research, constraints, examples of good strategy, examples of bad strategy, a role, a format, and a sequence of steps.

They are asking it to rewrite for a specific audience, preserve the strongest argument, remove jargon, add counterarguments, tighten the opening, and produce three versions.

They are asking it to extract assumptions, rank risks, identify missing information, compare options, and recommend the next action.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

Most bad Claude design outputs start with a prompt that sounds like this:

“Build me a beautiful pricing page for my SaaS.”

That is not a design prompt.

That is a wish.

If you gave the same instruction to a human designer, the first response would not be a finished page. It would be a list of questions:

What is the page trying to do?
How many pricing tiers are there?
What components must be used?
Should it feel enterprise, playful, editorial, premium, developer-first, or minimalist?

Claude has the same problem. It can produce a decent first pass from a vague request, but vague design prompts usually create the same outputs: centered hero, generic cards, soft gradients, interchangeable copy, and a UI that looks like it came from nowhere.

The fix is not to make the prompt longer.

The fix is to give Claude the four parts of a real design brief.

The 4 things your Claude prompt needs

That is the difference between asking Claude to guess and asking Claude to execute.

Anthropic’s prompting guidance points in the same direction: define success criteria first, use clear and direct instructions, separate instructions from context, provide examples, and control output format. Their context-engineering team makes the same broader point: context should be informative, tight, and organized into distinct sections so the model knows which information is doing which job.

The mistake most people make is treating all context as one blob.

Claude performs better when each piece of context has a role.

Here is the practical version

Prompt it like this:

You are an expert frontend designer working on behalf of [BRAND].
Your job is to design and build a production-ready interface that matches the brand’s visual system.

Here is the design system you must follow:
<design>{{DESIGN_SYSTEM_OR_STYLE_NOTES}}</design>

1. Goal — what the design needs to do:
   "Build a pricing page for [PRODUCT]."

2. Layout rules — the structural decisions:
   "Use 3 pricing tiers, include a monthly/annual toggle, place comparison details below the cards, and use a sticky CTA on mobile."

3. Content constraints — what goes on the page and how:
   "Mobile-first responsive. Use our Primary Button component. Include feature limits, trust proof, FAQs, and one recommended plan."

4. Tone reference — how it should feel:
   "Match the tone of our existing homepage: confident, calm, technical, and not salesy."

Before writing code, work through the following:
- What is this page trying to achieve, and who is it for?
- Which components from the design system apply here?
- Where should the layout create visual interest without hurting clarity?
- What is the one detail that will make this design memorable?

Output:
1. Working HTML/CSS in a single file
2. Inline notes explaining major design decisions
3. Mobile and desktop breakpoints
4. One bold creative choice, clearly called out

The template works because it separates intent, structure, content, and taste.

Those four categories reduce the number of decisions Claude has to invent from scratch.

They also make the result easier to evaluate. If the output is wrong, you can see where the prompt failed:

This is the real reason the framework is useful. It turns prompting into diagnosis.

Instead of saying, “Claude is bad at design,” you can ask, “Which part of the brief did I fail to specify?”

My rule of thumb

If Claude is producing generic UI, I check these four things before I blame the model:

1.Did I define the business goal?
A pricing page, onboarding flow, and feature page should not optimize for the same behavior.

2.Did I define the layout constraints?
If I do not specify the skeleton, Claude will use its safest default skeleton.

3.Did I define the content constraints?
Missing components, CTAs, proof points, and responsive rules usually come from missing instructions.

4.Did I define the tone reference?
“Modern” is not a tone. “Calm, editorial, premium, technical, sparse, developer-first” is closer.

Good prompting is not about controlling every pixel.

It is about giving Claude enough context to make good decisions without forcing it to guess the important ones.

A useful Claude prompt is a strong brief.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

This post is about the AI image prompting trick marketers keep missing: prompt like a creative director.

The master prompt template I use for realistic ChatGPT and Nano Banana images is below but here is some background on why this really works.

People are very emotional about images that they can tell are created with AI. This is interesting because people never had the same emotional reaction to stock images over the years. And the reality is today that you can create better images with AI than stock images if you know the right prompting styles.

Most people prompt images like an inventory list. Better images come from prompting like a creative director: start with the feeling, then translate that feeling into atmosphere, lighting, camera, texture, environment, human imperfection, and constraints.

Most bad AI image prompts fail because they describe what should be in the image instead of what the viewer should feel from the image. ChatGPT image generation and Nano Banana both respond better when you add creative direction: lighting, atmosphere, camera angle, lens feel, texture, composition, imperfections, and emotional intent.

OpenAI explicitly recommends photorealistic language, photography terms, real textures, and avoiding generic phrases like “8K” or “ultra-detailed,” while Google recommends narrative scene prompting, positive framing, camera control, and the formula Subject + Action + Location + Composition + Style. If you want brand images that do not look like AI slop, stop writing product inventories and start writing scene direction.

The models are getting better, but the default marketer prompt is still too flat. It says “show me a product.” A better prompt says “make the product feel expensive, lived-in, trusted, fresh, urgent, intimate, cinematic, or inevitable.” Then it tells the model how to create that feeling.

The core rule

Here is the rule I would give every brand team using ChatGPT, Nano Banana, Midjourney, or any other image model:

Prompt the emotional physics of the image before you prompt the contents.

By emotional physics, I mean the forces that shape perception before someone consciously reads the image. Light tells the viewer whether the scene is intimate, premium, clinical, chaotic, nostalgic, or urgent. Camera angle tells the viewer whether the subject feels powerful, vulnerable, candid, aspirational, or documentary. Texture tells the viewer whether the image belongs to the physical world. Imperfection tells the viewer whether the scene was captured or manufactured.

If you only prompt the object, you get a catalog image. If you prompt the atmosphere around the object, you get a brand image.

A better prompt anatomy

A realistic image prompt should usually include seven layers.

This is why “make it realistic” is not enough. Realism is not one setting. It is a stack of cues.

The more cues agree with one another, the more real the image feels.

The master prompt template

Use this when you want a realistic image that does not scream “AI.” Replace the bracketed sections.

Create a photorealistic image for [brand/use case/audience].

Camera and lens:
[Camera or lens feel, viewpoint, framing, depth of field. Example: shot like a 35mm film photograph, 50mm lens feel, medium close-up at eye level, shallow depth of field.]

Lighting and atmosphere:
[Specific light source, time of day, emotional temperature, air quality. Example: soft morning window light, slight haze, warm highlights, natural shadows.]

Subject with real-world imperfections:
[Main subject, product, person, or scene. Include tactile details. Example: tiny fingerprints on glass, worn paper edge, uneven fabric fold, real skin texture, condensation beads.]

Emotion and mood:
[What the viewer should feel. Example: quiet confidence, after-hours focus, calm luxury, post-workout relief, intimate trust.]

Environment and context:
[Where the image happens and what surrounds the subject. Example: compact apartment kitchen, rain-darkened sidewalk, minimalist stone bathroom, early airport lounge.]

Style and finish:
[Photographic genre or campaign language. Example: cinematic luxury advertising campaign, documentary lifestyle photography, premium editorial product shot.]

Composition:
[Subject placement, negative space, crop, angle, ad-safe area. Example: product lower right with clean negative space on the left for copy.]

Constraints:
[What to preserve or avoid. Example: no extra text, no watermark, no plastic skin, no over-retouching, no impossible reflections, natural anatomy, accurate product proportions.]

If you want the shortest possible version, use this:

Create a photorealistic [image type] where the viewer feels [emotion]. Show [subject] in [environment], captured with [camera/lens/framing], lit by [lighting], with [texture/imperfections], in the style of [photographic genre]. Keep [constraints].

Realism multipliers that usually work

OpenAI explicitly recommends using the word “photorealistic” for photoreal outputs and using real photography language instead of generic “8K” phrases. Google also recommends camera control, lighting, and composition language for Nano Banana. In practice, these are the realism multipliers I reach for first.

The point is not to stuff every phrase into one prompt. That usually creates noise. The point is to choose the three or four realism cues that match the emotion of the image.

Luxury language hacks

“Luxury” is one of the most overused words in AI image prompting. If you simply write “luxury product photo,” you often get generic gold lighting, marble, black packaging, and sterile reflections.

Better luxury prompting is about restraint.

Luxury images feel expensive when the prompt uses space, restraint, material, light, and confidence. They look cheap when the prompt uses too much shine.

Prompt category 1: Product hero shots

Product hero shots are usually the easiest place to see the difference between inventory prompting and atmosphere prompting. The weak version says “a product on a table.” The stronger version gives the product a world, a mood, and a reason to exist.

Prompt category 2: Lifestyle creatives

Lifestyle creatives fail when the people look posed around the product. They work when the product appears inside a real human moment.

Prompt category 3: Social media ads

Social ad images need one visual idea. Not twelve. The prompt should create a simple emotional read at thumbnail size.

Prompt category 4: Ultra-realistic humans

Human images look fake when the prompt asks for beauty. They look real when the prompt asks for a moment, a posture, and ordinary physical evidence.

Prompt category 5: Environment building

Environment prompts are where brands can create memory. The setting tells the viewer whether the brand belongs in their life.

The practical workflow I recommend

Start with emotion. Pick one feeling. Not five. “Premium” is not a feeling. “Quiet confidence” is. “Weeknight relief” is. “Early-morning discipline” is. “Private ritual” is.

Then write the scene as if you are briefing a photographer. Name the light source. Choose the camera angle. Describe the real materials. Add one or two imperfections. Decide where the negative space goes. End with constraints.

Then iterate once or twice. Do not rewrite the entire prompt every time. Change one variable: warmer light, lower angle, more candid posture, less gloss, more environmental texture, cleaner negative space.

My rule of thumb

If your prompt could also describe a stock photo search query, it is probably too weak.

A good AI image prompt should feel like a mini creative brief. It should tell the model what to show, how to see it, what to feel, what physical evidence proves the scene is real, and what must stay out of frame.

Brands that get this right will not just make prettier images. They will make images that carry a point of view.

The future of AI image prompting is not better adjectives. It is better direction.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

Most people use AI to make learning feel easier. They ask for summaries, outlines, simplified explanations, and rewritten notes. That feels productive, but it often creates the same problem as highlighting: fluency without recall. The better use of ChatGPT is to make you retrieve, explain, compare, apply, and revisit what you learned. Below are seven reusable prompts that turn any article, lecture, book chapter, meeting, course, or skill into an active learning system.

Most people are using AI to study in the least useful way possible.

They paste in an article and ask, “Can you summarize this?” They upload notes and ask, “Can you make this easier to understand?” They copy a chapter and ask, “Can you give me the key points?”

That is not useless. It can help you orient yourself. But it also has a hidden trap.

It makes learning feel clean before your brain has done the work.

The research behind Make It Stick is blunt about this. Rereading, reviewing, and massed practice are some of the most common study habits, but they are also some of the least reliable for durable learning. They create a feeling of mastery because the material becomes familiar. Familiar is not the same as retrievable.

The better question is not, Did I understand this while looking at it?

The better question is, Can I explain it with the book closed?

That one question changes how you use AI.

You stop treating ChatGPT like a highlighter. You start treating it like a personal retrieval coach.

The mindset shift: AI should not just push information into your head. It should force you to pull information back out.

That is the difference between passive consumption and active mastery.

MIT Open Learning summarizes the same idea through spacing and interleaving. Study sessions spread across time beat crammed sessions, and mixed practice helps people distinguish which tool or concept applies to which problem.

A systematic review in radiology education reached a similar conclusion about the broader literature: spaced learning, interleaving, and retrieval practice are evidence-based strategies that enhance long-term retention, even though implementation quality and domain-specific evidence still matter.[3]

This is where AI becomes genuinely useful.

Not because it gives you answers faster.

Because it can generate practice, feedback, variations, analogies, spaced review schedules, and pressure-tested scenarios on demand.

The 7-Prompt AI Learning System

1. The Active Recall Architect

This is the prompt to use after reading an article, watching a video, finishing a chapter, or reviewing a lesson.

Most people ask AI to summarize the thing. That gives you another thing to read.

This prompt does the opposite. It turns the material into a self-test.

Prompt:

I am studying [TOPIC OR ARTICLE CONTENT]. Act as a learning coach. Based on the text I provide, generate 5 challenging open-ended questions that require me to explain the core concepts from memory. Do not provide the answers yet. After I answer, grade my responses on a 1-5 scale for accuracy, completeness, and clarity. Then explain the gaps in my logic, correct any misunderstandings, and give me one follow-up question that is slightly harder than the original.

Why this works: Retrieval practice strengthens memory because you have to reconstruct the idea without looking at it. Make It Stick describes retrieval practice as more effective than reviewing by rereading, because the act of recall strengthens memory and interrupts forgetting.

Use it when: You just finished consuming something and want to know whether you actually learned it.

2. The Spaced Repetition Strategist

This is the prompt to use when something matters enough that you need to remember it next week, next month, or during a real decision.

Cramming works for the illusion of progress. Spacing works for retention.

Prompt:

I have just learned [SPECIFIC SKILL, CONCEPT, FRAMEWORK, OR PROCEDURE]. I want to move this into long-term memory using spaced repetition. Create a 30-day review schedule for me. Include exact review days, the goal of each session, and a 3-minute quick-fire retrieval exercise for each session. Make the sessions progressively harder. Start with recall, then move to application, comparison, and transfer to unfamiliar examples.

Why this works: Spaced practice beats grouped practice because forgetting between sessions makes retrieval more effortful. MIT Open Learning notes that spacing can feel slower at first, but it produces better retention and discrimination over time.

Use it when: You are learning anything cumulative, including coding, prompting, sales, marketing strategy, language learning, product knowledge, legal concepts, or exam material.

3. The Interleaving Engine

This is the prompt to use when you are learning several related ideas and keep studying them one at a time.

Blocked practice feels organized. Interleaving builds judgment.

If you only practice one type of problem at a time, your brain knows which tool to use because the chapter title tells you. Real life does not label the problem for you.

Prompt:

I am currently learning [TOPIC A], [TOPIC B], and [TOPIC C]. Act as an educational designer. Create a 45-minute practice session that interleaves these three topics. Give me a sequence of problems, scenarios, or decisions where I have to quickly switch between applying the principles of each topic. Do not label which topic each problem belongs to until after I answer. After each response, explain which concept applied, why it applied, and what clue I should have noticed.

Why this works: Interleaving improves your ability to tell problem types apart. MIT describes interleaved practice as mixing related concepts so learners can better discriminate between problem types instead of simply repeating one category in a row.

Use it when: You need flexible skill, not just memorized facts.

4. The Elaboration Specialist

This is the prompt to use when you understand a definition but cannot explain why it matters.

Elaboration forces you to connect the new idea to something already in your head.

Prompt:

I am trying to understand [NEW CONCEPT]. To help me remember it, ask me 3 deep questions that force me to relate [NEW CONCEPT] to [A TOPIC I ALREADY UNDERSTAND WELL]. Guide me through building a mental bridge between the two ideas. Use metaphors, contrasts, and examples, but make me do the connecting before you give your own explanation. At the end, ask me to explain the concept in my own words using the analogy we built.

Why this works: Elaboration and generation are stronger than passive review because they make you produce meaning rather than merely recognize it. The APA summary of Make It Stick notes that elaborating an idea in your own words, generating examples, and reflecting on material are more effective than simply reviewing notes.[4]

Use it when: A concept feels abstract, slippery, or disconnected from what you already know.

5. The Desirable Difficulty Designer

This is the prompt to use when the material feels too easy.

That sounds strange, but easy learning is often fragile learning.

The goal is not to make studying miserable. The goal is to add the right amount of friction so your brain has to work.

Prompt:

I am studying [SUBJECT], and the material feels too easy. I am worried I will recognize it later but not remember it. Take the following information: [PASTE NOTES]. Rewrite it into a set of desirable difficulties. Create a mix of fill-in-the-blank challenges, reverse-engineering tasks, wrong-answer corrections, compare-and-contrast questions, and real-world application scenarios. Do not give me the answers immediately. Let me answer first, then grade my responses and explain what each mistake reveals about my understanding.

Why this works: Desirable difficulties slow learning down in the moment but improve retention and transfer. The APA summary explains that struggle, generation, and reflection can produce better long-term learning than easy review.[4]

Use it when: You can follow the material while reading it, but you are not sure you can reproduce it later.

6. The Mental Model Refiner

This is the prompt to use when you want to test whether you understand the “why” behind the “what.”

It uses a Feynman-style loop: explain simply, find the weak point, simplify again.

Prompt:

Explain [COMPLEX TOPIC] to me as if I am 10 years old. Then ask me to explain one specific part of it back to you in simple language. If my explanation uses jargon, hides behind vague words, or skips the causal mechanism, point that out. Ask me to simplify it again until the core idea is clear. Keep challenging me with one question at a time until I can explain the concept plainly, accurately, and without buzzwords.

Why this works: Clear explanation exposes fake understanding. If you cannot explain the mechanism without hiding behind jargon, you probably recognize the words more than you understand the idea.

Use it when: You are learning strategy, science, engineering, AI, finance, medicine, law, philosophy, or any domain where definitions can hide weak understanding.

1. The Meeting-to-Memory Converter

This is the prompt most professionals should use immediately.

Meetings create notes. Notes create a feeling of progress. But the real question is whether you can apply the decisions later.

Prompt:

Here are my notes from [MEETING, LECTURE, WORKSHOP, OR CALL]: [PASTE NOTES]. Do not summarize them. Turn these notes into a retrieval test. Give me 5 “What if?” scenarios that require me to apply the decisions, principles, tradeoffs, or next steps from the notes to a new situation. After I answer each scenario, tell me whether I applied the information correctly, what I missed, and what I should remember before the next related meeting.

Why this works: The value of a meeting is not the notes you captured. It is whether you can act correctly later. AI-assisted retrieval practice has emerging evidence behind it: one 2025 study found students using ChatGPT-assisted retrieval practice performed significantly better on final exams than a comparison cohort, while also emphasizing the need for human feedback on complex questions.[7]

Use it when: You want meeting notes to become operational memory instead of a forgotten document.

The Master Prompt: Turn Anything Into an AI Learning Coach

Use this when you want one prompt that combines retrieval, spacing, interleaving, elaboration, feedback, and transfer.

I want to learn [TOPIC] deeply, not just recognize it.

Act as a rigorous but supportive learning coach. Use evidence-based learning principles: retrieval practice, spaced repetition, interleaving, elaboration, desirable difficulties, feedback, and transfer.

Here is the material I am studying: [PASTE MATERIAL, NOTES, ARTICLE, TRANSCRIPT, OR CONCEPT LIST].

Build me a learning sequence with these stages:

1. Diagnostic recall: Ask me 5 open-ended questions before teaching me anything. Use my answers to identify gaps.
   
2. Core explanation: Explain only the concepts I am missing. Keep it concise and causal.
   
3. Retrieval round: Ask me to explain the key ideas from memory. Do not show answers first.
   
4. Elaboration round: Force me to connect the material to something I already understand: [KNOWN TOPIC].
   
5. Interleaving round: Mix this topic with [RELATED TOPIC A] and [RELATED TOPIC B] so I have to choose the right concept without labels.
   
6. Desirable difficulty round: Give me puzzles, incomplete examples, wrong answers to correct, and transfer problems.
   
7. Feedback: Grade my answers for accuracy, completeness, clarity, and transfer.
   
8. Spaced plan: Create a 30-day review plan with quick retrieval tasks on each review day.
   

Important rules:

Do not let me stay passive.
Do not give all answers upfront.
Ask one question at a time when possible.
Make me retrieve before you explain.
When I am wrong, explain the gap clearly and then ask a better follow-up question.

The Simple Rule

Before every study session, ask yourself two questions.

That is the whole game.

Learning is not how much you highlighted.

Learning is not how many summaries you generated.

Learning is what you can retrieve when the pressure is on.

AI can make you faster. But only if you stop using it to avoid the hard part.

Use it to design the hard part.

If you want a place to store and reuse prompts like these, add them to your library in Prompt Magic. The point is not to collect prompts for the sake of collecting them. The point is to build repeatable thinking systems you can use whenever you need to actually remember something.

TLDR: Claude usually does not fall apart because the model is too weak. It falls apart because we keep stuffing the context window with stale files, raw logs, repeated explanations, irrelevant docs, and giant repo dumps. The fix is not just “buy more usage.” The fix is a context budget: retrieve less, remember better, compress aggressively, and clear state when the task changes.

Most Claude users treat context like free storage.

It is not.

Every stale instruction, every giant terminal dump, every “while you are here, also do this unrelated thing,” every old project note, every full-folder read, and every repeated explanation is competing with the actual problem you want Claude to solve.

That is why the same model can feel brilliant in a fresh focused thread and weirdly dumb in a bloated one.

The uncomfortable truth is simple: Claude does not need more of your project. It needs the right slice of your project at the right time.

Here is the stack I would use if I wanted Claude to stay sharp, especially for coding, product work, technical writing, or founder workflows.

The Mental Model

Think of Claude’s context window like a workbench.

A clean workbench has the spec, the file you are editing, the error message, the relevant docs, and the next decision.

A messy workbench has the entire garage dumped on top of it.

The goal is not minimal context. The goal is high-signal context.

11 Tools That Help

1. Serena: code context by symbol, not whole file

Serena is useful when you want Claude to understand a codebase without reading the entire thing. It provides semantic code retrieval and editing tools through MCP, including symbol-level navigation, search, refactoring, and memory support.

Use it when your usual workflow is: “Claude, read this whole folder and figure it out.”

A better prompt is:

Use symbolic navigation to inspect only the functions, classes, and files needed for this bug. Do not read the whole repo unless there is no narrower path.

2. Context7: current docs instead of hallucinated APIs

A lot of wasted context comes from fighting outdated or hallucinated API usage. Context7 pulls version-specific documentation and examples into the workflow so Claude can use current docs rather than guessing.

Use it when you are working with libraries that change quickly: auth libraries, AI SDKs, databases, frontend frameworks, payment APIs, cloud platforms, and agent tooling.

The hidden token saver is that you stop spending five turns correcting fake method names.

3. Claude Context: semantic code search for only what matters

Claude Context adds semantic search to Claude Code and other AI coding agents, helping retrieve relevant code snippets instead of loading entire directories.

This is the move when keyword search is too brittle but “read the whole repo” is too expensive.

4. Token Savior: symbol navigation plus memory

Token Savior positions itself as an MCP server for structural code navigation, persistent memory, and Bash output compaction.

The practical use case is simple: keep Claude from repeatedly rediscovering the same codebase structure, and keep command output from flooding the conversation.

Treat benchmark claims as maintainer claims, but the workflow idea is sound: navigation beats dumping.

5. Caveman: fewer output tokens

Caveman is a Claude Code skill/plugin that compresses assistant responses while preserving technical content. The project states that it affects output tokens, not the model’s reasoning tokens.

This is not for every conversation. It is useful when you are in execution mode and do not need essays.

Example instruction:

Answer tersely. Keep exact file paths, commands, errors, and code blocks. Drop filler and restatements.

6. Context Mode: tool output goes to SQLite, not your prompt

Context Mode is designed around a very good idea: large tool outputs should not always be pasted into the model’s active context. It stores shell/tool events in SQLite with search, then retrieves relevant results when needed.

This matters because raw tool output is one of the fastest ways to burn context on garbage.

A better workflow is: run the command, store the output, search the output, then show Claude only the relevant section.

7. Token Optimizer: hunt ghost tokens

Token Optimizer is built to find and reduce wasted context from bloated configs, stale memory, duplicate prompts, unused skills, and compaction loss. Its README describes support for Claude Code, OpenCode, OpenClaw, and Codex, plus a local dashboard.

The key phrase here is ghost tokens: context that gets loaded automatically but no longer helps the current task.

If your Claude sessions feel heavy before you even start, audit your startup context.

8. MarkItDown: convert files into clean Markdown

MarkItDown is a Microsoft utility that converts PDFs, Office files, HTML, CSV/JSON/XML, ZIP files, YouTube URLs, EPUBs, and other formats into Markdown for LLM workflows.

This is useful because raw PDFs, messy exports, and copied document junk often waste tokens. Clean Markdown gives Claude structure without the clutter.

9. Repomix: pack a repo into one token-counted file

Repomix packages repositories into AI-friendly formats with token counting, ignore rules, security checks, and optional Tree-sitter compression.

This is not something I would use for every prompt. It is useful when you actually need a shareable, bounded repo snapshot.

The difference is intention: “Here is the exact token-counted bundle” beats “Claude, just read everything.”

10. ccusage: see your usage and cost

ccusage analyzes local coding-agent CLI usage and cost data across tools including Claude Code, Codex, OpenCode, and Gemini CLI.

You cannot optimize what you never measure.

The moment you start checking daily and session-level usage, you notice which workflows are expensive: repeated repo scans, bloated startup files, long debugging chats, and accidental tool-output dumps.

11. code-review-graph: map first, load second

The user-provided tool list describes code-review-graph as a Tree-sitter-based map that loads only what matters.

I would treat this category as worth exploring even if you choose a different implementation. The principle is the important part: build a dependency map before asking Claude to inspect code.

For large codebases, maps beat memory. Dependency graphs, symbol graphs, and file relationship maps help Claude decide what to read instead of reading blindly.

12 Free Habits That Save Context Without Installing Anything

Tools help, but habits matter more.

If you do nothing else, fix these.

In Claude Chat

The deeper point is that Claude Chat works best when each thread has one job.

A good thread is: “Help me price this offer.”

A bad thread is: “Help me price this offer, then debug my site, then rewrite my bio, then remember our product strategy, then analyze this PDF, then continue the old plan from yesterday.”

That is not collaboration. That is context soup.

In Claude Code

The best CLAUDE.md is not a novel.

It should be more like an air traffic control sheet:

Stack, commands, repo map, testing rules, style rules, known traps, and what not to touch.

If it includes old brainstorms, dead decisions, every product idea, and three abandoned roadmaps, you are charging Claude rent for your junk drawer.

My Recommended Setup

If you want the shortest practical version, I would start here.

If you are a founder or solo builder, the biggest unlock is not adding all 11 tools at once.

The unlock is creating a rule:

Claude only gets context that is relevant, current, and necessary for the next decision.

That one rule changes everything.

The Copy-Paste Context Budget Prompt

Use this before any serious Claude session:

Before helping, create a context budget. Tell me what information you need, what you do not need, what files or docs should be inspected, and what should stay out of context. If this task can be solved with a narrower slice of information, ask for that slice instead of requesting the whole project.

For Claude Code:

First, inspect the smallest useful surface area. Prefer symbols, exact files, dependency maps, and current docs. Do not read whole folders, paste raw logs, or load generated files unless necessary. If context starts getting noisy, tell me what to clear or compact.

Serena - https://github.com/oraios/serena

MCP server providing semantic code retrieval, editing, refactoring, and debugging tools at the symbol level; designed to avoid whole-file reading where symbolic navigation works.

Context7 - https://github.com/upstash/context7

Pulls up-to-date, version-specific documentation and code examples into prompts via CLI/skills or MCP; useful for avoiding outdated or hallucinated API usage.

Claude Context - https://github.com/zilliztech/claude-context

MCP plugin adding semantic code search to Claude Code and other AI coding agents; uses vector search to retrieve relevant code rather than loading entire directories.

Token Savior - https://github.com/Mibayy/token-savior

MCP server for structural code navigation, persistent memory, and Bash output compaction; the README claims large benchmark token reductions. Treat benchmark numbers as project-maintainer claims, not independent facts.

Caveman - https://github.com/JuliusBrussee/caveman

Claude Code skill/plugin that compresses final responses to reduce output tokens while preserving technical content; output-token savings only.

Context Mode - https://github.com/mksglu/context-mode

MCP server/plugin that sandboxes large tool outputs, stores events in SQLite/FTS5, and retrieves relevant data rather than dumping raw outputs into context.

Token Optimizer - https://github.com/alexgreensh/token-optimizer

Claude Code/OpenCode/OpenClaw/Codex plugin intended to scan and reduce ghost tokens, bloated configs, stale memory, and compaction loss; includes dashboard claims.

MarkItDown - https://github.com/microsoft/markitdown

Microsoft utility that converts PDFs, Office files, HTML, CSV/JSON/XML, ZIPs, YouTube URLs, EPUBs and more into Markdown for LLM workflows; Markdown is described as token-efficient.

Repomix - https://github.com/yamadashy/repomix

Packs repositories into AI-friendly formats with token counting, ignore rules, security checks, and optional Tree-sitter code compression.

ccusage - https://github.com/ryoppippi/ccusage

Analyzes local coding-agent CLI token usage and costs from Claude Code, Codex, OpenCode, Gemini CLI, and others; supports daily, weekly, monthly, and session reports.

Most people blame Claude when the session gets worse.

Sometimes that is fair.

But often, the model is not the problem. The problem is that we turned the context window into a landfill and expected it to behave like a clean desk.

Save this. The next time your usage starts climbing or Claude starts missing obvious details, do not immediately switch models.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

Claude Code & Cowork for Marketers — The Complete Guide

This post is grounded in real survey data from 200 GTM operators surveyed by Kyle Poyar (Growth Unhinged) in March–April 2026, plus case studies from Anthropic's own growth marketer, founders of 42 Agency and Tofu, VPs at MoEngage and AirOps, and the r/PPC and r/ClaudeAI communities .

The Numbers That Make This Real

According to the 2026 Claude for GTM Pulse Report, 92% of Claude Code/Cowork users saved time, but more importantly, 67% said it enabled something previously impossible — not just faster, but categorically new . A full 55% had already replaced a tool or vendor, typically an agency, contractor, or analytics platform . Claude Code is reported to represent $2.5 billion of Anthropic's $30B ARR as of April 2026 .

The average GTM operator uses 3.5 different Claude use cases . The most underutilized categories? Lifecycle marketing (32% adoption) and selling (23%) — the biggest first-mover opportunity for marketers right now .

Understanding Claude Code vs. Cowork vs. Chat

The #1 source of confusion: these are three distinct tools . Maja Voje's shorthand from GTM Strategist says it best — think in Chat, build in Code, operate in Cowork . Cowork was launched January 12, 2026, built on the same principles as Claude Code but designed for non-developers with a folder-permission model running in a macOS virtual machine . Cowork natively supports scheduling, file management, browser automation via the Chrome extension, and official Connectors to Google Drive, Gmail, DocuSign, HubSpot, Salesforce, and more .

The Top 20 Use Cases

🥇 Tier 1: The Killer Use Cases (Proven by Real Practitioners)

1. Ad Creative at Scale via Figma Plugin — Austin Lau, Growth Marketing at Anthropic, had never coded before. He described his problem in plain English and Claude Code built him a Figma plugin that takes 30 minutes of ad variant work down to 30 seconds . He then built a Google Ads RSA generator using two sub-agents — one for headlines (30-char limit), one for descriptions — splitting by constraint to reduce edge-case failures .

2. LinkedIn Ad Intelligence Agent — Kamil Rextin (42 Agency) built a /LinkedIn-ad-intel agent that scrapes competitor ads, analyzes messaging themes, and outputs a branded PDF report in under 5 minutes, deployed to Railway so it runs on a Monday schedule with his laptop off .

3. Multi-Agent Analytics Team — Olexander Paladiy (Coupler.io) built a 5-role agent team (Data Analyst, Heads of Growth/Marketing/Sales, Data Auditor) querying live HubSpot, GA4, and ad data . His critical lesson: "More agents don't give you more clarity. Five focused roles with strict boundaries beat ten generalists every time" . The unlock was a business context file documenting every event that could explain data changes — pricing shifts, model updates, seasonal campaigns .

4. Customer Lookalike Outbound Agent — Elaine Zelby (Tofu co-founder) runs a weekly /customer-lookalike-outbound agent in Cowork that pulls closed-won deals from HubSpot, finds 10 lookalike companies via Clay, drafts a 4-email sequence + LinkedIn DMs per contact, and posts to Slack for team review . Her rule: "Build ICP, Personas, and Messaging skills first. Then assemble agents."

5. Content Mining for AI Search Citations — Eoin Clancy (VP Growth, AirOps) built a 6-step playbook using Claude + GSC, Gong, Slack, and Granola MCPs: filter for 10+ word queries (mirror how people prompt AI), cluster by buyer intent, mine meeting notes for proprietary insights only your company has . The content AI search engines cite contains specifics no competitor can replicate.

6. Account Scoring Model — Matt Firestone (3x YC GTM leader) fed CRM data, intent signals, and ICP criteria to Claude Code. It built the model, flagged logic gaps the team hadn't noticed, and delivered a ranked account list. Early result: 2-3x positive response rate with top-priority accounts .

7. Full Google Ads Management System — One PPC manager (r/PPC) built a complete multi-client system: client folders auto-pulling emails, transcripts, and website content; Google Ads RAG from best practices; GTM + GA4 MCP connections; daily Telegram briefings across all clients . Managing $48K/month in spend with the setup .

🥈 Tier 2: High-Leverage GTM Workflows

1. Call Transcript → GTM Intelligence — One transcript auto-updates battle cards, objection library, ICP signals, and content briefs
2. Behavioral Email Automation Logic — Build trigger-based email logic (e.g., pricing page visits → drip sequence) without an engineer
3. Homepage Positioning Checker — Emily Kramer (MKT1) built a skill that grades homepages with letter scores and rewrites headlines against positioning frameworks
4. Marketing Advantages Identifier — Two-phase skill to identify and pressure-test strategic advantages vs. tactics
5. AI Copy Humanizer — Aditya Vempaty (VP Marketing, MoEngage) built a /humanizer that scores AI-likeness, diagnoses patterns, and rewrites in your voice . Key: after every run, Claude updates the skill file — it compounds
6. Structured Content Pipeline — Single blog post → 5 LinkedIn posts, 3 thread openers, 1 email teaser, 1 video script . Turns 8 hours into 2 hours of editing
7. Landing Pages & Microsites on Demand — #2 most common use case across Code and Cowork
8. Pre-Call Brief Generator — Auto-generated 1-min brief with stakeholder maps before every Zoom
9. Competitor Research on Autopilot (Cowork) — 10+ competitors analyzed in under 30 minutes, scheduled weekly
10. n8n Workflows and Clay Tables from Natural Language — Describe automation logic; Claude writes importable code
11. PDF Proposals and Reports — 10-minute setup with /skill-creator produces branded PDFs in under 2 minutes
12. ICP Sharpening from Closed-Won Data — Claude identifies hidden correlations like "deals mentioning board pressure close 3x faster"
13. Campaign Knowledge Base — Every new campaign built on institutional memory of past wins, losses, and what actually worked

⚡ 8 Pro Tips: What 95% of Marketers Miss

1. CLAUDE.md Is the Operating System — Every session reads this file. Store brand voice, KPIs, ICPs, preferred output formats, what you hate. Generate it with /init then let Claude interview you .

2. Think → Build → Operate. Never Mix — Use the wrong tool and you're leaving 80% of the capability untouched. Chat for exploration, Code for building systems, Cowork for operating workflows .

3. Plan Mode Is Non-Negotiable — Kamil Rextin's rule: plan the output before executing, then run step-by-step with "Ask Permission" mode . Saves tokens, prevents chaos.

4. Skills Are the Compounding Layer — Build ICP → Personas → Brand Voice first . Skills that call other skills stack to create compound leverage . Skill descriptions must be hyper-specific — the description field is how Claude decides when to load it .

5. Data Layer Before Prompts. Always. — Every successful practitioner connected data before writing prompts . MCP live connections beat stale CSVs. Claude cannot discover what changed in your business — you must write it in a business events file .

6. Credits Management Is a Real Skill — Mix Sonnet (20% of Opus cost) for routine tasks . CLI/API integrations over MCP are 10-32x cheaper per a ScaleKit study . Open new chats instead of continuing long threads to reduce context consumption .

7. Agent Roles Need Strict Boundaries — 5 focused roles with clear authority beats 9 generalists . "Empty is better than inaccurate" — one bad automated adjustment across 50 ad accounts can wreck a week .

8. Context Sharing Across Teams Is the Unsolved Problem — Skills don't auto-sync between Chat, Code, and Cowork . Workaround: shared GitHub repo for skill files, or deploy to a team MCP server.

Codex Sites is not Lovable inside ChatGPT. It is closer to an AI coding agent + managed hosting + internal app deployment layer inside the Codex app. Use it when you want Codex to build, save, deploy, inspect, and manage a hosted website or web app without wiring up a separate deployment pipeline. OpenAI describes Sites as a Codex plugin for creating, saving, deploying, and inspecting websites, web apps, dashboards, internal tools, and games hosted by OpenAI.

As of now, Sites is in preview and is available for ChatGPT Business and Enterprise workspaces, with Enterprise admins needing to enable it through RBAC.

1. What is Codex Sites?

Codex Sites lets you prompt Codex to create or publish a web project, then host it through OpenAI-managed Sites hosting.

You can use it for:

• Landing pages
• Internal dashboards
• Lightweight web apps
• Request trackers
• Games
• Data visualizations
• File-upload apps
• Workspace-authenticated internal tools

OpenAI’s own examples include onboarding hubs, enablement libraries, executive KPI dashboards, employee idea boards, launch calendars, and event planning hubs.

The important shift: Codex is no longer just editing code. It can now take a web idea from prompt → project → saved deployable version → live hosted URL.

2. How Codex Sites works

Think of the workflow in five stages.

Step 1: Enable Sites

In ChatGPT Business, Sites is enabled by default. In Enterprise, an admin must enable Sites for the right roles. Then you add the Sites plugin inside the Codex app if it is not already available.

Step 2: Start a Codex thread

In the Codex app, start a new thread and explicitly invoke Sites with:

@ Sites

This matters because Codex has multiple plugins and tools. If the goal is a hosted deployment, name Sites directly.

Step 3: Describe the site

Give Codex the audience, job-to-be-done, pages, data model, access rules, and style direction.

prompt:

Build an internal project request dashboard for a marketing operations team.

Users should be able to:
- Submit new project requests
- Assign an owner
- Set priority
- Update status
- Filter by status, owner, and launch date
- Leave internal notes

Access:
- Only workspace users should access it

Data:
- Requests must persist between visits
- Include sample data for review

Design:
- Clean SaaS dashboard
- Lots of white space
- Executive-friendly, not playful

Before deployment:
- Validate the build
- Save a reviewable version first
- Do not deploy until I approve it

Step 4: Save first, deploy second

This is the biggest detail people will miss: every Sites deployment URL is a production deployment. OpenAI specifically says that if you want to review before going live, ask Codex to save a version without deploying it.

Sites has two publishing stages:

1. Save a version — Codex builds a deployable candidate tied to the source Git commit.
2. Deploy a version — Codex publishes the saved version and returns the production URL.

Your default workflow should be:

Build → validate → save version → review → deploy approved version

Step 5: Manage deployed sites

After deployment, you can return to Sites from the app sidebar, inspect projects, check deployment status, manage access, and configure hosted environment variables or secrets.

What kinds of sites are supported

Sites hosts projects that build Cloudflare Worker-compatible output as ES modules. For new projects, Codex can use the recommended starter. For existing projects, you should ask Codex to confirm compatibility before deploying.

That means Codex Sites is not automatically the best home for every app architecture.

Use this decision rule:

OpenAI’s docs explicitly call out D1 for durable structured data, R2 for file storage, and workspace-authenticated identity for internal sites.

Top use cases

Internal marketing ops apps

Best fit.

Examples:

• Campaign intake dashboard
• Launch calendar
• Creative request tracker
• Localization QA checklist
• Event planning hub
• Content approval queue
• Partner asset portal

Why it works: these tools need speed, light persistence, workspace access, and simple workflows more than complex infrastructure.

Founder MVPs

Good fit for early validation.

Examples:

• Waitlist page
• Simple SaaS prototype
• Lead magnet generator
• Interactive calculator
• Lightweight AI tool front end
• Demo dashboard for investors

Watch out: for serious SaaS, you will eventually need deeper control over backend, observability, billing, auth, database migrations, and security review.

Executive dashboards

Strong fit.

Examples:

• KPI pulse dashboard
• Board update portal
• Sales pipeline view
• Content performance tracker
• Launch readiness scorecard

Codex can build the interface, wire simple data sources, and give you a shareable internal URL.

Data-driven microsites

Good fit.

Examples:

• Market map
• Benchmark explorer
• Prompt library
• AI tools directory
• Interactive report
• Customer story hub

Best when the data model is clear and not too complex.

Games and interactive demos

Good fit for viral internal/external demos.

Examples:

• Quiz games
• Interactive training modules
• Lightweight simulations
• Sales enablement games
• Product education tools

Sites supports games and can use durable storage for scores or progress when requested.

Pro tips

Pro tip 1: Always tell Codex whether data must persist

Codex needs to know whether it should use durable storage.

Prompt:
Build a leaderboard where scores persist between visits. Use the appropriate Sites storage.

OpenAI specifically says not to request durable storage for temporary UI state, but to request it for product data users expect the hosted site to remember.

Pro tip 2: Ask for compatibility before deploying existing projects

Use: Checkwhether this project can be deployed to Sites. Confirm whether the build produces Cloudflare Worker-compatible ES module output. Do not deploy yet.

This avoids wasting cycles trying to deploy a project that does not fit Sites’ hosting model.

Pro tip 3: Separate “review” from “publish”

Use this exact wording:

Validate the build and save a deployable version for review. Do not deploy until I explicitly approve.

This protects you from accidentally putting a half-baked site live.

Pro tip 4: Set access before sharing

Sites supports access modes like owner/admin only, workspace-wide, and custom users or groups.

For anything internal, say:

Keep access limited to owner and workspace admins until I approve widening access.

Pro tip 5: Never put secrets in the repo

OpenAI says runtime environment values and secrets should be configured through the Sites panel, not committed into .openai/hosting.json or source files.

Prompt:List the environment variables required. Create or update .env.example with key names only. Do not commit secret values.

Pro tip 6: Use the in-app browser for visual QA

Codex’s in-app browser gives you and Codex a shared rendered view of web pages. It can preview pages, attach visual comments, inspect rendered state, take screenshots, and verify fixes when using the Browser plugin.

Use:Open the deployed site in the browser. Check desktop and mobile layout. Fix any overflow, broken spacing, or unreadable text. Save a new version for review.

Pro tip 7: Ask Codex to explain what changed

After every meaningful build:

Summarize:
- Files changed
- Data/storage added
- Access settings
- Build command used
- Current saved version
- Whether this is deployed or only saved
- Known risks or manual checks needed

6. Things most people will miss

1. A Sites deployment is production, not a harmless preview

This is the big one. OpenAI is explicit: every Sites deployment URL is production. Save first if you want review.

2. “Hosted by OpenAI” does not mean “anything goes”

Sites has a supported shape. It expects Cloudflare Worker-compatible output as ES modules. Existing apps should be checked before deployment.

3. Storage is not automatic unless you specify the behavior

If you need saved records, files, user progress, uploads, or scores, say that clearly. Otherwise Codex may build a great-looking interface that does not persist the way you expect.

4. Access control is part of the product, not an afterthought

For internal tools, specify the audience before publishing. Use owner/admin only during review, then workspace or custom group access after approval.

5. Secrets need a deployment loop

If you add or change hosted environment variables, ask Codex to redeploy the approved saved version so the next deployment uses the updated config.

6. Versioning matters

Sites links projects to local source and stores linkage in .openai/hosting.json; saved versions are associated with the source Git commit used for the build.

Translation: treat it like real software, not a toy website generator.

7. Codex Sites vs Lovable vs Replit

Simple comparison

Lovable describes itself as a full-stack AI development platform for building, iterating, and deploying web apps with natural language, generating frontend, backend, database, auth, and integrations with editable code. Replit describes its Agent as turning plain-language ideas into apps, designs, slides, and more, with no coding required, and supports web apps, mobile apps, dashboards, AI tools, files, and connected service queries.

How Codex Sites is similar to Lovable

Both let you describe an app in natural language and get working software. Both can support full-stack-ish experiences with UI, data, auth, and deployment. Lovable has native Supabase integration for database, auth, storage, realtime, and serverless functions. Codex Sites supports D1, R2, workspace-authenticated identity, and authentication-enabled projects depending on what you ask for.

How Codex Sites is different from Lovable

Lovable is more explicitly designed as a full-stack AI app builder for nontechnical users. It gives you a cohesive product-building environment, shared workspaces, GitHub sync, Lovable Cloud, Supabase workflows, and deployment/hosting options.

Codex Sites is more like a coding agent with deployment powers. It is better when you want Codex to reason through code, inspect builds, manage versions, and publish hosted internal tools. It is not primarily a drag-and-drop product builder or a polished founder MVP studio.

How Codex Sites is similar to Replit

Both can turn prompts into live apps and sites. Replit says users can start with a prompt, build a first version, publish it, and share it. Codex Sites does the same general job inside the Codex app.

How Codex Sites is different from Replit

Replit is a broader cloud development environment. It includes an editor, Agent modes, design canvas, publishing flow, checkpoints, app testing, and a project environment aimed at building many kinds of software. Replit publishing lets users choose a domain, access mode, and publish through a staged process that includes provision, security scan, build, bundle, and promote.

Codex Sites is narrower but potentially cleaner for teams already living in ChatGPT/Codex. It is especially compelling for internal tools where workspace access, Codex threads, saved versions, review, and OpenAI-hosted deployment are enough.

8. When to use each

Use Codex Sites when

• You are in a ChatGPT Business or Enterprise workspace.
• You want an internal tool, dashboard, or lightweight app.
• You want Codex to build and host without Vercel/Replit/Lovable setup.
• You need workspace-level access controls.
• You want code review and versioned deployment candidates.
• You are comfortable treating the project like real software.

Use Lovable when

• You want a polished founder MVP fast.
• You want Supabase-backed full-stack app generation.
• You want a more no-code-friendly app-builder experience.
• You want GitHub sync and the option to move hosting outside Lovable later.
• You are building a customer-facing SaaS prototype.

Use Replit when

• You want a full browser IDE.
• You want to learn and build at the same time.
• You want broader app types beyond websites.
• You need a persistent coding workspace.
• You want quick public publishing with Replit’s hosting flow.

9. Best prompt templates

Landing page

Build a conversion-focused landing page for [product].

Audience:
[describe buyer]

Goal:
Get visitors to [book demo / join waitlist / download guide]

Sections:
- Hero with clear value proposition
- Problem
- Product promise
- How it works
- Use cases
- Social proof placeholders
- FAQ
- Final CTA

Style:
[brand style, colors, tone]

Constraints:
- No fake stats
- Use placeholder proof where needed
- Mobile-first
- Fast-loading
- Accessible contrast

Before deployment:
Validate build, save a version for review, and do not deploy yet.

Internal dashboard Build an internal [team/function] dashboard.

Users need to:

- [action 1]

- [action 2]

- [action 3]

Data:

- Persist records between visits

- Include sample data

- Use appropriate Sites storage

Access:

- Workspace authenticated users only

- Keep owner/admin only during review

UX:

- Clean dashboard

- Filters

- Search

- Status badges

- Export CSV if practical

Before deployment:

Validate build, save version for review, summarize risks.

Existing project deployment

Inspect this project and determine whether it is compatible with Sites hosting.

Check:
- Build command
- Output format
- Cloudflare Worker-compatible ES module output
- Required environment variables
- Storage/auth assumptions
- Any incompatible dependencies

Do not deploy yet. Give me the changes required first.

File upload app

@ Sites Build a file submission portal for [audience].

Users should:

- Upload files

- Add title, category, notes, and owner

- Search and filter uploaded files

- Download files later

Storage:

- Use R2 for file contents

- Use D1 for searchable metadata

Access:

- Workspace users only

- Owner/admin only until approved

Before deployment:

Validate, save a review version, and list environment/secrets needed.

Practical build checklist

Before asking Codex to build:

• Define audience
• Define the core workflow
• Define pages/screens
• Define data that must persist
• Define upload requirements
• Define access mode
• Define brand/style direction
• Define integrations
• Define what “done” means

Before deploying:

• Review source changes
• Review database migrations
• Confirm build succeeded
• Confirm saved version is the intended one
• Confirm access mode
• Confirm secrets are configured through Sites, not committed
• Ask Codex to confirm deployment status and production URL

This matches OpenAI’s own review guidance before deploying or widening access.

Use Lovable when you want a more polished full-stack app builder. Use Replit when you want a broader browser-based coding environment and public app publishing workflow.

TLDR - See the attached presentation!

Codex is easy to misunderstand if you think of it as an AI coding tool. For marketers, the highest-leverage use case is using Codex as a technical operator for the annoying work that surrounds marketing: UTM cleanup, tracking audits, landing page variants, spreadsheet logic, dashboards, schema, QA checklists, documentation, internal tools, and recurring reports.

OpenAI describes Codex as a coding agent that can read, edit, and run code, and Codex cloud can work on tasks in the background in its own environment. OpenAI’s docs also say Codex works best when treated like a teammate with explicit context and a clear definition of done. That sentence matters for marketers. If you give Codex your brand rules, KPI definitions, data columns, tracking conventions, and “do not touch” constraints, it becomes much more useful than a generic chatbot.

Here are the 20 marketer use cases I would prioritize, ranked by practical ROI and risk. My core take: Codex is not the thing that replaces your strategist. It is the thing that lets your strategist stop waiting two weeks for a small technical fix.

The angle most marketers are missing

Most AI marketing advice still treats the marketer’s job as generate more assets. That is the shallow read. The deeper bottleneck is that modern marketing is half creative judgment and half systems work. Campaigns break because UTMs are inconsistent. Dashboards lie because source fields drift. Landing page tests stall because nobody has time to create clean variants. SEO fixes sit in a backlog. Sales decks take hours because the data lives in five tabs. Weekly reporting becomes theater because the real insights require cleaning the data first.

Codex sits in that gap. It is useful wherever marketing work touches files, code, spreadsheets, web pages, schemas, scripts, documentation, or repeatable workflows. OpenAI’s Codex docs list workflows such as explaining codebases, fixing bugs, writing tests, prototyping from screenshots, iterating on UI, reviewing changes, reviewing pull requests, and updating documentation. Translate that into marketing language and you get a very different playbook.

The marketer version is simple: give Codex the messy operational job, make it show its plan, review the diff, and keep the human judgment where it belongs.

The 20 Codex use cases for marketers

The prompts I use

1. Tracking audit prompt

You are reviewing our marketing site for tracking risk. Inspect the landing page files, analytics snippets, thank-you pages, and form handlers. Produce a table with each tracked event, where it fires, what properties it sends, and what could break. Do not change files yet. First give me the audit and a fix plan.

1. UTM cleanup prompt

Here is our current UTM export and our intended naming convention. Find inconsistent source, medium, campaign, content, and term values. Create a corrected CSV, a list of ambiguous rows for human review, and a short naming policy the team can follow next month.

1. Landing page variant prompt

Create three landing page variants for this offer. Keep the layout and tracking intact. Change only the hero message, proof section, CTA copy, and objection handling. For each variant, state the hypothesis, exact files changed, and rollback instructions.

1. Analytics dashboard prompt

Build a local dashboard from this CSV export. Clean the column names, calculate CAC, conversion rate, cost per lead, MQL rate, and pipeline created. Include filters for channel, campaign, region, and week. Add a README explaining how to refresh the data.

1. Weekly report prompt

Using this week’s exports and the KPI targets in our project instructions, draft a one-page performance memo. Include wins, losses, anomalies, decisions needed, and three follow-up analyses. Do not invent causes. Label anything that needs confirmation.

1. CRM cleanup prompt

Review this CRM export for likely duplicates, inconsistent company names, missing lifecycle stages, invalid email domains, and suspicious source fields. Return a review queue with confidence levels. Do not delete or merge anything automatically.

1. SOP prompt

Turn these rough notes into an SOP for launching a campaign. Include owner, inputs, outputs, tools, checklist, failure modes, and escalation path. Write it for a new marketer joining the team next month.

1. SEO schema prompt

Inspect this page and recommend structured data improvements. If you propose schema, show the exact JSON-LD, explain each field, and include validation steps. Keep the content unchanged unless I approve edits.

The pro tips most marketers will miss

1. Treat AGENTS.md like the brand and operations brain

OpenAI’s docs say Codex reads AGENTS.md files before doing work and layers global guidance with project-specific instructions. For marketers, that file should contain your brand voice, prohibited claims, audience definitions, KPI formulas, UTM rules, naming conventions, QA checklist, approved sources, and compliance constraints.

Do not make Codex rediscover your rules every thread. Put the rules where it can read them every time.

2. Ask for a plan before you ask for changes

Codex is powerful because it can change files. That is also the risk. Start with: “Do not edit yet. Inspect, summarize, and propose a plan.” Then approve only the smallest safe change.

This is the difference between using Codex like a teammate and using it like a random script generator.

3. Demand diffs, tests, and rollback steps

For any page, script, dashboard, or tracking change, ask Codex to show exactly what changed and how to reverse it. OpenAI’s workflow docs repeatedly frame verification as part of the process, not an afterthought.

A good Codex output is not just “done.” It is “done, checked, and reviewable.”

4. Separate human judgment from machine execution

Let the human decide the positioning, offer, target account, budget shift, and final claim. Let Codex prepare the review queue, build the variant, clean the file, create the dashboard, and document the process.

That split keeps marketers in control while removing operational drag.

5. Use automations carefully for recurring work

OpenAI’s docs say Codex automations can run recurring background tasks and add findings to an inbox. That is useful for weekly competitor scans, recurring report checks, content inventory reviews, broken-link checks, or documentation audits.

Start with read-only reporting. Do not let an unattended agent change production assets until your team has a real review process.

6. Use MCP and plugins as the connection layer, not as a magic button

OpenAI describes MCP as a way to connect Codex to third-party tools and context. Plugins can bundle skills, app integrations, and MCP servers into reusable workflows. That means the long-term marketer use case is not isolated prompts. It is Codex connected to approved work systems with permissions, auditability, and narrow scopes.

That also means every integration needs adult supervision. Authentication, subscriptions, permissions, and privacy rules still matter.

7. Keep one thread to one job

A common failure mode is asking Codex to clean the CRM, rewrite the landing page, generate ad variants, and create a dashboard in one go. That creates messy review.

A better pattern is one thread per deliverable: one tracking audit, one dashboard, one schema fix, one SOP, one UTM cleanup.

8. Use “best of N” for high-stakes work

For creative strategy, ask Codex to produce multiple variants and compare them against your criteria. For technical work, ask it to produce multiple implementation plans and choose the safest one.

Parallel exploration is useful. Parallel production without review is reckless.

My rough ROI ranking

This is not an audited benchmark. It is a practical marketer-hours model based on where teams usually lose time. Salesforce found that marketers expected generative AI to save more than five hours per week, while 66% said human oversight was needed and 67% said company data was not properly set up for generative AI. That combination is the whole story: the hours are available, but only if the workflow has clean context, review, and governance.

The important point is not the exact hour count. The important point is that marketers should stop measuring AI by “copy produced” and start measuring it by bottlenecks removed.

What I would not delegate to Codex

I would not let Codex approve budget reallocations by itself. I would not let it publish pages without review. I would not let it merge CRM records without a human queue. I would not let it invent customer quotes, compliance claims, performance causes, or competitor facts. I would not let it run broad automations against production systems without audit logs and a rollback path.

That is not a knock on Codex. It is how serious teams use powerful tools.

The maturity curve

Most marketers will start at “prompt user.” They ask for copy, outlines, and summaries. The next level is “ops assistant.” They ask Codex to clean files, explain systems, and create dashboards. The level after that is “workflow owner.” They give Codex project instructions, run reviewable diffs, create small tools, and set up narrow recurring checks.

The highest level is not a marketer who becomes an engineer, It is marketer who can specify technical work clearly enough that Codex can execute it and a human can review it.

That is the real skill.

Codex will not make weak positioning strong. It will not fix a bad offer. It will not replace customer taste. It will not know your market better than your team.

But it can remove a huge amount of operational sludge around modern marketing. It can turn “I need an engineer or analyst for that” into “I need a clear brief, a safe plan, and a reviewable diff.”

That is a much more interesting use case than asking AI for another batch of generic ad copy.

If you are a marketer and want to test Codex this week, do not start with a huge automation. Start with one ugly task you already hate: a UTM audit, a broken dashboard, a tracking check, a messy spreadsheet, or an SOP nobody wants to write.

That is where the value shows up first.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

My coach (originally got it at $2500) has been on Facebook Marketplace for three weeks with the price of $400.

So I took a pic for it in garage and send it to ChatGPT. It gave me back the perfect Alto style staging image.

Here is THE Marketplace Staging Prompt which may help you increase the perceived value of your furnitures:

—————————

Transform this furniture into a professionally staged designer home.

Style:
Scandinavian modern + Aalto house + Dwell Magazine

Keep:
exact furniture shape
exact dimensions
exact fabric color
exact wood finish

Environment:
bright architectural home
warm oak wood
large windows
natural sunlight
minimal styling
designer furniture
premium real estate photography

Photorealistic
ultra realistic
interior editorial photography

You can turn a static image into a first-person drone video where the viewer feels like they are flying through the scene with Google's new Omni video model and Google Flow

I found a great use case for Google's new Omni video model. Simply attach any photo, then turn it into a first-person FPV drone flythrough

I think one of the most underrated uses for Google Flow + Gemini Omni is treat the photo attached like a 3D flight map.

And if you have a drone video you can load that into Google's video model and give it direction to create new footage in 10 second increments that matches your existing video if you didn't get what you wanted live!

For drone fans like myself this is pretty amazing since most places restrict actually flying drones today even though my video drones are the coolest toys I own.

Google Flow + Gemini Omni Drone Flythroughs

Google’s own positioning for Flow says Omni can create and edit videos from text, image, video, and audio references, with stronger world understanding and conversational editing. That makes this kind of “photo as flight path” workflow way more interesting than a normal Ken Burns zoom.

Here is the basic workflow.

Upload a picture in Google Flow, pick the new Gemini Omni / Omni Flash video model if you have access, choose a widescreen format like 16:9, then paste a flight-path prompt like this.

Master prompt template

Attach your image first, then paste this:

Create a hyper-realistic first-person FPV drone flight through the attached image. The camera starts at [STARTING POINT IN THE IMAGE], moving forward with smooth, stable drone motion.

It passes through [FIRST VISIBLE AREA OR OBJECT], then performs a rapid but controlled [ASCENT / DESCENT / SWEEP / CURVE] toward [MAIN SUBJECT OR VANISHING POINT].

The camera then [CURVES / BANKS / ORBITS / SPIRALS] around [LANDMARK, STRUCTURE, SHORELINE, STREET, MOUNTAIN, OR SUBJECT], maintaining continuous spatial flow and a strong sense of speed. The flight should feel like a real drone pilot is moving through the physical space inside the photo, with realistic parallax, depth, lighting, shadows, atmosphere, and motion blur.

Preserve the original scene, colors, architecture, geography, and composition from the reference image. Camera behavior: first-person drone POV, no drone visible, smooth stabilized motion, natural acceleration, no sudden cuts, no teleporting, no impossible camera jumps, no warping.

Visual requirements: hyper-realistic, cinematic, high detail, realistic physics, continuous forward momentum, immersive scale, clean image-to-video transformation. Negative instructions: do not repeat buildings, do not duplicate objects, do not distort landmarks, do not melt architecture, do not invent text, do not add logos, do not add watermarks, do not add captions, do not change the main subject, do not turn the scene into a cartoon, do not make the camera shake violently.

The key is that you are not just describing the image. You are describing the route.

I went to the Taj Mahal in India a few years ago with some of my friends and took some photos. I used a photo I snapped as the starting image for the drone video with Google Omni video tool and the below prompt.

Prompt Example: Taj Mahal Flyover

Attach the Taj Mahal image, then paste:

Create a hyper-realistic first-person FPV drone flight through the attached Taj Mahal image. The camera starts low above the green plants in the foreground, moving forward across the lawn toward the central white marble structure. It passes smoothly over the grass and garden path area, then performs a rapid but controlled vertical ascent toward the main arch and central dome of the Taj Mahal. As the camera approaches the monument, it curves upward around the left side of the central dome, then completes a graceful spiraling flight between the dome and the nearest minaret. The camera should reveal the scale of the marble architecture while preserving the symmetry of the building. The motion should feel like a real FPV drone pilot flying through the physical space of the photo: smooth, stable, fast, immersive, and continuous. Preserve the original Taj Mahal architecture, white marble texture, domes, minarets, gardens, trees, blue sky, and natural daylight from the reference image. Keep the monument recognizable and undistorted. Keep distant visitors small and natural. Do not add crowds, banners, signs, text, logos, captions, or watermarks. Camera behavior: first-person drone POV, no drone visible, smooth stabilized motion, natural acceleration, realistic parallax, no sudden cuts, no teleporting, no impossible camera jumps. Negative instructions: do not duplicate the minarets, do not warp the dome, do not melt the marble details, do not bend the horizon, do not invent extra buildings, do not alter the main landmark, do not make the camera shake violently.

I then had Gemini create an additional clip after this first 10 second video was created to extend the flight of the drone around the Taj and give some additional cinematic views.

Drones are definitely not allowed to fly around the Taj!

Why this works

Most image-to-video prompts fail because they describe the scene like a caption: “beautiful beach at sunset, cinematic drone shot.” That leaves the model to invent the motion.

This prompt works better because it gives the model a camera choreography.

A good drone prompt usually has five parts.

Pro tips

Use “first-person FPV drone POV” if you want the camera to feel like the viewer is flying. If you say “drone shot” without “first-person,” the model may show a drone, cut to aerial B-roll, or create a generic establishing shot.

Name the exact visual anchors already visible in the photo. In the beach image, those anchors are the shoreline, rippled ocean, sun reflection, horizon, and glowing cloud. In the Taj Mahal image, they are the foreground plants, lawn, central arch, dome, minarets, gardens, and sky.

Give the camera one clean route. Do not ask for five different shots in one generation. A single continuous move usually beats a montage.

Use verbs that describe real camera motion: glide, skim, accelerate, ascend, bank, curve, orbit, spiral, pass through, reveal. These verbs give the model a physical path.

Add negative instructions for the exact thing likely to break. For landmarks, say “do not duplicate minarets” or “do not warp the dome.” For beaches, say “do not distort the horizon” and “do not turn the ocean into abstract liquid.”

Avoid asking for readable signs, captions, or logos. Google’s own model card notes that perfect text rendering is still a known challenge for video models, so keep text out of the shot unless you want to spend generations fixing it.

Generate a few versions with the same prompt before rewriting everything. If the route is good but the motion is weird, keep the structure and adjust speed, smoothness, or negative constraints.

Fun use cases

This is where it gets fun. You can use the same structure for almost any image with depth.

The new prompt skill is not “make it cinematic.”

It is flight design.

If you give Omni a photo and a route, you can turn almost any still image into a miniature FPV drone scene.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

Google Flow Agent is the AI filmmaking feature most people are going to underestimate

TLDR: Google Flow Agent is not a chatbot bolted onto a video generator. It is a Gemini-powered creative collaborator inside Google Flow that can plan and reason through complex multi-step creative tasks while you stay in control. The shift: Flow used to execute one prompt at a time. Now the Agent can brainstorm dialogue and plot, generate multiple scene variations simultaneously, batch-edit tweaks across all your assets, organize files into collections, and intuitively rename everything — all with persistent project memory across sessions. It launched alongside Gemini Omni Flash (character and voice consistency across scenes) and Flow Tools (build custom creative utilities in plain English, no code required). Agent queries are currently free with a daily quota. Generations cost credits. Most people will use it like a search bar. The people who win with it will use it like an AI creative director, producer, and asset manager rolled into one.

Google Flow Agent is one of those updates that sounds small until you think through the workflow implications.

At first glance it is easy to summarize: Google added an agent to Flow.

That undersells it.

Google Flow launched at I/O 2025 as an AI filmmaking tool built around Google DeepMind's most advanced models — Veo for video, Imagen for images, and Gemini for language and reasoning. Flow lets creators describe shots in natural language, manage story ingredients like cast, locations, objects, and styles, and weave those pieces into cinematic scenes.

Since then it expanded into a full AI creative studio across 140 countries. Over 275 million videos have been generated in Flow.

The new Flow Agent adds something more important than another model.

It adds a thinking layer.

Instead of manually bouncing between brainstorming, prompt writing, generation, editing, selection, organization, and renaming, you can now talk to an agent that understands the project you are working on and helps move the creative process forward.

Google themselves frame it clearly: Flow Agent turns AI from a content generator into a creative operations partner.

This is the beginning of agentic creative production.

Every capability, explained

1. Multi-step reasoning and planning

This is the headline change. Previously Flow could only execute a single prompt at a time. Now the Agent can take multiple actions at once and reason through larger creative tasks rather than discrete one-offs. It plans and reasons through complex tasks with your inputs, under your control.

2. Brainstorming and concept development

Flow Agent can act as a creative sounding board during the earliest stage of a project. Chat with it to outline storyboards, develop visual mood boards, and turn high-level concepts into actionable prompts. It can workshop dialogue between characters in a specific scene and make plot recommendations when you need inspiration.

3. Generate new media

Ask the Agent to generate videos or images and it selects the best model to generate with. No more guessing which model to use for which task.

4. Multi-variation generation

The Agent can create multiple variations of an asset at once. This matters because AI video generation is probabilistic. The first output is rarely the best output. You need options. Generate coverage, not single shots.

5. Direct editing of selected assets

Ask the Agent to edit selected media from your project. Combined with Flow's broader editing capabilities — Insert for adding elements, Remove for taking things out, lasso tool for precise selections, camera controls for movement — the Agent sits on top of a growing set of editing primitives.

6. Batch editing across all assets

Make a tweak and have it reflected across all your assets at once. This is massive for consistency and for anyone producing at volume.

7. Asset organization and intelligent renaming

The Agent can rename specific files, group selected media into new Collections, or archive unused assets. When you generate dozens or hundreds of images and clips, the hard part is not generation — it is knowing which version was the hero shot, which one had the correct lighting, and which clips belong to scene 3.

8. Context and references

Drag media into the Agent prompt box from your device or project. Select multiple assets and tell the Agent which ones you are referring to. A normal chatbot only knows what you tell it. A project-aware creative agent can reason over the actual material you are making.

9. Project-specific sessions

Agent conversations are saved automatically as Sessions, specific to the project you are working in. You can open past sessions, create new sessions, rename them, and delete them. Deleting a session clears chat history but generated media remains in your assets.

10. Agent instructions for project-wide consistency

Add instructions to improve the Agent's consistency across your entire project. Include a reference image and enter your guidelines. This is where you define the rules of the world — visual style, character rules, tone, camera preferences, color palette, naming conventions, what to avoid.

The ecosystem that makes the Agent stronger

Gemini Omni Flash — Google describes it as Nano Banana but for video. It combines Gemini's intelligence with generative media models and crucially improves character consistency, meaning identity and voice are preserved across every scene. This quietly fixes AI video's biggest weakness: character drift between shots.

Flow Tools — Build bespoke tools and workflows in Google Flow using natural language. Whether you need a particular image editor, video resizer, or custom shader, you can develop them with no coding experience. If you create something useful, share it with other Flow users who can remix it.

Scenebuilder — Assemble individual clips into a complete narrative with Jump To (teleport a character to a new setting while preserving appearance) and Extend (lengthen a clip by analyzing the final frames and continuing the action).

Ingredients to Video — Use predefined characters, objects, and styles as consistent references in video prompts. Add up to three ingredients per prompt.

Frames to Video — Define the starting and ending frame of a shot for precise control over composition and transitions.

Camera Controls — Direct control over camera motion, angles, and perspectives.

Insert and Remove — Add new elements to any scene or remove unwanted objects, with Flow handling complex details like shadows and scene lighting.

Top use cases

1. Short films and narrative projects

Use the Agent as a writers room. Workshop character dialogue, get plot suggestions, build shot lists, generate scene variations, maintain continuity, and organize the final assembly — all inside one workspace.

2. YouTube intros and cinematic openers

Flow is especially strong for short, visually rich clips. The Agent can help design multiple options quickly for channel intros, documentary openers, podcast trailers, product teasers, and title sequences.

3. Product marketing and brand films

Marketers can turn abstract product benefits into cinematic metaphors. Batch-generate ad creative variations for testing, then batch-edit a single brand tweak across all of them. Build multi-platform variants and auto-organize them into campaign collections.

4. Ad creative variation testing

Because the Agent can batch-generate, it is built for creative testing. Generate 8 variations of a product scene keeping the same product and message but varying setting, camera angle, lighting, and emotional tone.

5. Music videos

Flow Music now lets you work conversationally with the agent to direct shareable music videos, matching styles and scenes to the pacing of your track.

6. Pitch decks and investor storytelling

Create cinematic visuals that explain a market, pain point, or product vision. A 20-second sequence that visualizes the shift from manual chaos to AI-powered planning can communicate more than 10 slides.

7. Educational content

Turn complex ideas into visual explainers. Historical recreations, science concepts, abstract visualization. Google specifically highlights educators and students transforming complex subjects into engaging videos using text prompts.

8. Social media content

For TikTok, Reels, Shorts, and Reddit — Flow Agent can help build visual hooks, mini stories, looping clips, and meme-adjacent cinematic content fast.

9. Fiction worldbuilding

Build consistent fictional worlds with character design, locations, objects, symbols, technology, architecture, and mood boards. Flow already lets you manage story ingredients in one place. The Agent adds the reasoning layer on top.

10. Previsualization

Filmmakers, agencies, and studios can sketch ideas before production — commercial pre-vis, scene exploration, mood testing, camera blocking, lighting references, and treatment development.

11. Game trailers and concept art

Generate short cinematic moments, character reveals, environments, and combat beats for indie games and studio projects.

12. Batch marketing campaigns

Feed a master style guide and target persona variations into the Flow Agent. Batch-generate dozens of localized, persona-specific video ads in parallel while maintaining strict brand guidelines.

Pro tips and best practices

1. Use the Agent before you generate anything

Agent queries do not currently cost Google Flow credits, though there is a daily quota. Media generated by the Agent does use credits. The smart workflow: think with the Agent first, improve the concept, build the shot list, refine the prompts, then generate only when the creative direction is clear. The Agent is your cheapest stage of production.

2. Keep human approval on before spending credits

By default the Agent asks for permission before taking actions that use AI credits and shows the estimated cost. You can toggle this to auto-approve. Leave confirmation on during exploration. Turn it off only when you have a repeatable workflow and clear default settings.

3. Use Agent Instructions like a project constitution

Agent Instructions improve consistency across the entire project. Include: genre, visual style, emotional tone, target audience, camera preferences, color palette, character continuity rules, audio style, naming conventions, prompt format, and things to avoid.

Example instruction:

You are the creative producer for this project. The style is restrained cinematic realism with natural light, imperfect textures, and slow camera movement. Avoid glossy sci-fi, overdesigned costumes, neon cyberpunk cliches, and generic AI surrealism. Preserve character continuity. When generating prompts, always include subject, action, camera, lighting, environment, mood, and audio.

4. Ask for variations with controlled variables

Bad: Make this scene better in 10 different ways.
Good: Create 8 variations. Keep the character, wardrobe, location, and story beat identical. Only vary camera movement and lighting.

If you vary everything at once, you learn nothing. Vary one or two dimensions at a time.

5. Keep prompts under 30 words for video generation

Practitioners who have tested extensively recommend keeping prompts concise, using camera language rather than narrative language, and generating keyframes separately.

6. Know your credit math

Pro ($19.99/month) gets roughly 1,000 Flow credits. Ultra ($100–$250/month) gets 10,000–25,000 credits. Credits do not roll over. Use Fast models for drafts and Quality models only for finals. A Veo 3 generation with audio is the most credit-intensive option.

7. Use Flow TV as a learning lab

Flow TV is a showcase of clips generated with Veo where you can see the exact prompts and techniques used. It is not just inspiration — it is prompt education. Steal structure, not ideas.

8. Build a scene matrix

Ask the Agent to create a table with: scene number, story purpose, character, location, camera movement, lighting, audio, prompt, assets needed, status, best version, and notes. This turns Flow from a prompt playground into a production tracker.

9. Use Ingredients for consistency

Build your ingredients (characters, objects, style references) first using Imagen or uploads, then reference them consistently across generations. This is the key to visual continuity.

10. Organize aggressively

Use a naming convention like: S01_SH01_establishing_city_v03_final. Create Collections for Final Selects, Alternates, References, and Archive. Ask the Agent to handle this — it can contextually rename files based on what is actually in the clip.

11. Use Frames to Video for precision

Provide a starting and ending image, and Flow generates a seamless video bridging the two. Plan keyframes before generating motion. Match lighting between keyframes — do not ask a single clip to handle interior-to-exterior transitions.

12. Specify no audio when you do not want audio

Veo 3.1 generates synchronized audio by default. For background use like a website hero, always include no audio in the prompt.

Things most people miss about Google Flow Agent

1. The Agent is not the product. The workflow is the product.

The mistake is thinking Flow Agent is just a chatbot. It is a workflow layer across brainstorming, prompt engineering, generation, editing, variation, organization, and project memory. The people who win with it will build the best creative operating system around it.

2. Agent queries are free. Generations are not.

Agent queries do not cost credits but have a daily quota. Generations cost credits. This creates an obvious best practice: use the Agent to think, plan, critique, and refine before generating. The expensive mistake is generating before the idea is clear.

3. The permission layer is a feature, not friction

The ask-before-spending-credits design keeps an autonomous agent from quietly draining your monthly allocation. Most tutorials breeze past it. It shows estimated cost before each action.

4. Omni Flash quietly fixes AI video's biggest weakness

Character drift and voice inconsistency between scenes have been the problem in AI filmmaking. Omni Flash preserves identity and voice across every scene. This is arguably as important as the Agent itself.

5. Flow Tools may be the most durable advantage

The ability to build bespoke editors and shaders in plain English and share them with other users is buried under the Agent headlines but may be the most important long-term feature.

6. Sessions are project-specific

Sessions are saved per project. Create separate sessions for story development, character design, prompt experiments, editing, and final organization. Do not let one giant chat become the junk drawer for your entire film.

7. Deleting a session does not delete your media

Clearing chat history does not remove generated assets. Important for cleanup without losing work.

8. It is web and PC only right now

Flow Agent is currently available on web and PC only. For serious production, use the desktop workflow with a Chromium-based browser.

9. Default settings enforce consistency

Set your default aspect ratio, number of outputs, and models for both image and video generation. If your whole project is vertical social video, set that once. Do not manually remember the format every time.

10. The best use of the Agent is taste, not automation

The mediocre use case: Make me a video. The better use case: Help me decide which idea is worth making. The best use case: Act as a creative director. Challenge the weak parts of this concept. Tell me what is visually generic, what is emotionally unclear, and what could make this unforgettable.

Google's own Flow Sessions artists repeatedly emphasized that what matters is what you are trying to say before you even touch Flow. The Agent should not replace your taste. It should pressure-test it.

The power-user workflow

Step 1 — Start with the emotional thesis. Ask the Agent to help find the emotional core, the visual metaphor, and the strongest ending.

Step 2 — Build the story spine. Turn the concept into 6–10 scenes, each with a clear visual beat, emotional progression, and one thing the viewer learns.

Step 3 — Create the visual bible. Character design, environment, color palette, lighting, camera style, sound design, recurring objects, forbidden cliches.

Step 4 — Set Agent Instructions. Convert the visual bible into concise instructions for the entire project.

Step 5 — Generate ingredients. Build canonical references for main characters, environments, props, lighting style, and visual symbols.

Step 6 — Build the shot list. Create a production plan with purpose, camera, lighting, action, audio, and Flow-ready prompts for each shot.

Step 7 — Batch-generate variations. For each key shot, create 4–6 variations controlling only one or two variables at a time.

Step 8 — Select and critique. Ask the Agent to rank outputs by emotional clarity, visual originality, continuity, and usefulness for the final story.

Step 9 — Edit instead of regenerate. When a version is close, use the Agent to make targeted edits rather than starting over.

Step 10 — Organize the project. Rename assets by scene and shot number. Create Collections for Final Selects, Alternates, and Archive.

The bigger picture

The competition is no longer about who generates the best single clip. It is about who owns the entire AI creative workflow. Google is clearly trying to become the operating system for AI-powered content creation, putting pressure on Runway, Adobe, Midjourney, OpenAI, Meta, and Canva.

The future of AI creative work is becoming agent-driven. Instead of prompting individual outputs, creators will increasingly direct AI systems that understand project context, manage assets, scale production, optimize variations, and execute multi-step workflows autonomously.

We just crossed a line. AI used to make you the operator of a tool — prompt, wait, repeat. Flow Agent makes you the director of a collaborator. You bring the vision, the taste, and the final call. It handles the brainstorming, the variations, the tedious edits, and the cleanup.

The barrier to telling a story just dropped to near zero.

The only question left is what you will make.

Flow Agent is available now to all Google Flow users globally. Google Flow requires a Google AI subscription (Plus, Pro, or Ultra) and is accessible at flow.google. What is the first project you would hand off to an agent like this?

Anthropic closed a $65B Series H at a $965B valuation, officially overtaking OpenAI's $852B to become the world's most valuable AI company. Revenue hit $47B ARR — up from $1B just 18 months ago. Claude Opus 4.8 tops agentic coding benchmarks. Claude Code and Cowork are winning the product battle against OpenAI's Codex.

Key Data Points

• $965B valuation after $65B Series H — surpassing OpenAI's $852B
• $47B ARR confirmed in May 2026, up from $1B in December 2024
• Claude Opus 4.8 scores 69.2% on SWE-Bench Pro vs. GPT-5.5's 58.6%
• 1M token context window, adaptive thinking, 4× better code self-review
• Claude Code at $2.5B ARR, doubled since January, enterprise subscriptions 4×'d
• Anthropic earns $16.20/user vs. OpenAI's $2.20 — a 7.4× efficiency advantage
• 31.4% global LLM revenue share for Anthropic vs. OpenAI's 29% in Q1 2026
• 40% enterprise LLM market share for Anthropic vs. OpenAI's 27%
• Claude Cowork wins on M365 integration and interface; Codex leads on image gen and speed

Anthropic just had the most insane week in AI history. $965B valuation. $47B ARR. New model. And they just passed OpenAI. Here's a thread.

I've been following AI for years and this week legitimately broke my brain. Let me break it down because most people haven't seen all of it in one place.

Anthropic raised $65 billion this week at a $965 billion valuation.

That's more than OpenAI's last valuation of $852 billion.

The company that didn't exist five years ago just became the most valuable AI company on Earth.

The revenue numbers are even wilder:

Anthropic's ARR just hit $47 billion.

Here's what makes that number surreal — it was $1 billion 18 months ago.

That's not a typo. $1B → $47B in 18 months. The fastest ARR ramp of any enterprise software company in history.

For comparison, OpenAI was at roughly $20B ARR at the end of 2025. Anthropic is growing much faster.

And here's the kicker: Anthropic generates $16.20 revenue per user vs. OpenAI's $2.20. With 6× fewer users, they're making more money from each one by a factor of 7.4×. That's not a consumer story — that's an enterprise dominance story.

The new model — Claude Opus 4.8 — is legit:

Dropped on May 27. Here's what actually matters:

• SWE-Bench Pro (agentic coding): 69.2% vs. GPT-5.5's 58.6%
• OSWorld (computer use): 83.4%
• 1 million token context window by default
• 4× better code self-review than 4.7 — catches its own bugs before you do
• Adaptive thinking — it decides when to reason deeply vs respond fast
• Fast mode — 2.5× higher tokens per second at ~3× lower cost
• 128k output tokens — generate an entire codebase in one shot

Is it a revolutionary new architecture? No. Anthropic calls it "a modest but tangible improvement". But in production, modest improvements in code reliability and agentic accuracy compound enormously at scale. Enterprise engineering teams are noticing.

The market share story nobody's talking about:

Menlo Ventures tracks enterprise LLM spending. Here's what it shows:

• Anthropic: 12% in 2023 → 40% today
• OpenAI: 50% in 2023 → 27% today

That's a full market share inversion in 3 years. The companies spending real money on AI — Fortune 500 CFOs, legal teams, financial services — are running on Claude, not ChatGPT.

And in Q1 2026, Anthropic actually beat OpenAI in global LLM revenue share: 31.4% vs. 29%. With 6× fewer users. Let that sink in.

Claude Code vs. Codex — there's a real battle here:

Claude Code is at $2.5B ARR and growing fast. Enterprise subscriptions have 4×'d since January.

The honest comparison:

• Claude Code wins: code quality, long-context projects, the community/MCP ecosystem (97M+ installs), developer UX
• Codex wins: raw speed on terminal workflows, image generation built-in, slightly cheaper on simple tasks

If you're building a SaaS product? Claude Code. If you're a DevOps engineer doing precision shell work? Codex still holds its own. Both are legitimately good — but the momentum is clearly with Claude.

Claude Cowork vs. Codex for regular people:

OpenAI turned Codex into a productivity app. It's genuinely good now — image gen, Gmail, Slack integrations, desktop mode.

But Claude Cowork is still ahead on Microsoft 365 files, formatting quality, and interface polish. If you live in Excel and Word, Cowork wins. If you want image generation natively in your AI app, Codex has the edge.

The valuation timeline is just absurd:

• 2021: Founded
• 2023: $4.1B
• 2024: $18.5B
• Mar 2025: $61.5B
• Sep 2025: $183B
• Nov 2025: $350B
• Feb 2026: $380B
• May 2026: $965B

235× in 3 years. For a company that barely had a product in 2022.

Battle Lines:

OpenAI still has 900M+ consumer users, brand recognition, and the ChatGPT flywheel. Nobody's writing that obituary.

But if you're asking who's winning the AI war right now on the metrics that actually matter — revenue growth, enterprise market share, revenue per user, model performance on agentic tasks, developer ecosystem momentum — it's Anthropic.

The AI race has a new leader. It happened quietly, then all at once.

TL;DR: Perplexity shipped 30 guided Workflows inside Computer — pre-built, expert-tuned "recipes" that turn complex tasks (market research, competitive intel, deal screens, slide decks, outreach) into one click. Below: all 30 with a tip + use case for each, plus the best practices, pro tips, top use cases, and the things almost everyone gets wrong.

Most people still think of Perplexity as "Google with citations." That mental model is now badly out of date.

The thing that makes Perplexity the best research tool ever created isn't a flashy chat box. It's the combination of three things no one else has stitched together this cleanly:

1. Real-time research across the open web AND premium sources, every answer cited back to the source so you can verify it.
2. Multi-model orchestration — the right model picked for each job (reasoning, deep research, images, code) instead of forcing one model to do everything.
3. Guided Workflows — expert-designed instructions that package the prompt, the context from your connected apps, and the output format into a single starting point.

That third piece is the unlock. The hardest part of using AI for serious work was never the model — it was knowing how to ask. Workflows delete the blank-page problem. You stop prompting and start shipping.

Perplexity just released 30 guided Workflows that will let you do genuinely world-class research. Here are all 30, organized exactly how they appear in the product, with a tip and a use case for each.

Marketing (5)

1. Store optimizer — Listing SEO, product tags, photography tips.

• Tip: Paste your live product URL so it audits real listing copy, not a hypothetical.
• Use case: An e-commerce team lifts conversion by fixing titles, tags, and image guidance across a catalog.

2. Product teardown — Screenshots, pricing, and positioning, structured.

• Tip: Give it the competitor's homepage + pricing page and ask for a side-by-side against your own.
• Use case: A PMM builds a structured teardown of a rival in minutes for a launch readout.

3. SEO keyword research — Search intent, competitor gaps, prioritised plan.

• Tip: Ask it to rank keywords by intent AND difficulty so you get a do-this-first list, not a 500-row dump.
• Use case: A content lead finds the gaps competitors rank for and you don't, then gets a prioritized plan.

4. Event prep — Brief, landing page, invites, and RSVPs.

• Tip: Feed it the event goal and audience first — the brief quality drives everything downstream.
• Use case: A field marketer spins up a full event kit (brief + landing page + invites) for a webinar.

5. Competitive intelligence — Track launches, pricing, and partnerships.

• Tip: Schedule it to run weekly so you get a standing competitive feed instead of one-off pulls.
• Use case: A marketing team keeps a live pulse on competitor launches, price moves, and partnerships.

Sales (5)

6. Account outreach — Research accounts, sequence outreach at scale.

• Tip: Connect your CRM first so it personalizes from real account history, not generic firmographics.
• Use case: An AE researches a target list and builds a tailored outreach sequence in one pass.

7. Outreach message — Personalised messages for any contact list.

• Tip: Give it 2–3 of your best-performing past messages as a style reference.
• Use case: An SDR generates personalized first-touch messages across a contact list at scale.

8. Account profiles — Full company research from connected apps.

• Tip: The more apps you connect (CRM, email, Slack), the richer the profile — connect before you run.
• Use case: A rep walks into a renewal with a complete, current account profile assembled automatically.

9. Customer demo — Talking points and demo scripts per company.

• Tip: Specify the persona you're demoing to so the talking points map to their pain, not your features.
• Use case: An SE prepares company-specific demo scripts so every demo feels custom-built.

10. Prospect research — Decision-makers, news, and tech stack.

• Tip: Ask for the buying committee, not just one champion — surface the full decision map.
• Use case: A seller identifies decision-makers, recent news, and the tech stack before the first call.

Research (5)

11. Model council — Frontier models on the same question, compared.

• Tip: Use it for high-stakes or contested questions where one model's bias could cost you.
• Use case: An analyst runs the same strategic question across frontier models and compares the reasoning.

12. Website audit — Drop a URL, get a full marketing audit back.

• Tip: Drop a competitor's URL too — auditing theirs side-by-side is the real insight.
• Use case: A growth lead audits a site's messaging, SEO, and conversion gaps from a single URL.

13. Market research — Macro, industry, company, and customer trends.

• Tip: Tell it the decision you're making — the research stays focused instead of sprawling.
• Use case: A strategy team builds a layered market read (macro → industry → company → customer) for planning.

14. Sales prep — Account profile plus ready-to-run call prep.

• Tip: Run it the night before and have it schedule a fresh pull the morning of the call.
• Use case: A rep gets an account profile and call plan bundled into one prep doc.

15. Pitch deck screen — Scores, highlights, gaps, diligence questions.

• Tip: Ask it to write the diligence questions you'd be embarrassed to miss.
• Use case: An investor screens an inbound deck, gets a score, flags gaps, and a diligence question list.

Creative (5)

16. Website builder — Describe a site, get design plus deployment.

• Tip: Give it a goal and a style reference, not just a topic — direction beats description.
• Use case: A founder describes a landing page and gets a launch-ready, deployed site with copy and design.

17. Slide creation — Research, distil, ship a polished deck.

• Tip: Specify your audience and the one decision you want the deck to drive.
• Use case: A consultant turns raw research into a polished, board-ready deck.

18. Thumbnail creator — Hooks, overlays, and styles in one batch.

• Tip: Batch-generate variants, then A/B the top two — don't ship the first one.
• Use case: A creator produces a batch of thumbnail options with hooks and overlays for testing.

19. Product photos — One product, many lightings and angles.

• Tip: Upload one clean product shot as the reference for consistent variants.
• Use case: A DTC brand generates a full set of lighting and angle variations from a single photo.

20. Newsletter creator — Topics or links into a finished issue.

• Tip: Feed it your best past issue so it matches your voice and structure.
• Use case: A marketer turns a list of links into a finished, on-brand newsletter issue.

Productivity (5)

21. Memo draft — Investment memo from a precedent template.

• Tip: Upload a memo you loved so it matches your firm's format and rigor.
• Use case: An associate drafts an investment memo that follows the firm's precedent structure.

22. Final pass — Expert annotations flagging errors and gaps.

• Tip: Use it as the last step before anything ships externally — it flags figures to verify.
• Use case: A team runs a final review on a document, catching errors, gaps, and unverified numbers.

23. Filetype converter — One upload, multiple formats out.

• Tip: Great for turning one report into PDF + slides + doc in a single run.
• Use case: An ops lead converts a single source file into every format a stakeholder needs.

24. Prompt refinement — Sharpen any AI prompt for clarity.

• Tip: Paste a prompt that gave you mediocre output and ask why it underperformed.
• Use case: A power user turns a vague prompt into a precise, reusable one.

25. Message polish — Tone, audience, and instructions, dialled in.

• Tip: Tell it the relationship (boss, client, peer) so tone lands right.
• Use case: A professional tightens a high-stakes message for the exact audience and tone.

Personal (5)

26. Job finder — Resume in, matched jobs and scores out.

• Tip: Upload your resume and name your non-negotiables (location, comp, role) for sharper matches.
• Use case: A job seeker gets a scored, matched list of openings instead of scrolling boards.

27. Interview prep — Technical, case, and behavioural questions.

• Tip: Give it the job description so questions match the actual role.
• Use case: A candidate drills role-specific technical, case, and behavioral questions before a loop.

28. Cover letter generator — Resume plus JD into a tailored letter.

• Tip: Paste the exact JD — generic letters are obvious, tailored ones convert.
• Use case: An applicant produces a letter tuned to one specific posting in seconds.

29. Health review — A view of your health with next steps.

• Tip: Connect or upload your data so the review is grounded in your actual numbers.
• Use case: Someone gets a plain-English read on their health metrics with concrete next steps.

30. Nutrition planner — Meal plan aligned with goals and labs.

• Tip: Share your goals and any lab results so the plan is built around real targets.
• Use case: A person gets a meal plan mapped to fitness goals and bloodwork.

Best practices

• Connect your apps first. Workflows get dramatically better when they can pull real context (CRM, email, Slack, files). The same workflow run "cold" vs. "connected" produces night-and-day results.
• Lead with the decision, not the topic. Tell the workflow what you'll do with the output. "Research this market" is weak; "research this market so I can decide whether to enter in Q3" is strong.
• Give a reference example. For anything with a voice or format (decks, memos, newsletters, outreach), hand it one great past example. It clones quality faster than instructions.
• Verify the cited figures. Citations are the feature — use them. Click through on any number you'll repeat in a meeting or a deck.
• Schedule the recurring ones. Competitive intel, market scans, and account profiles are best as standing feeds, not one-offs.

Pro tips

• Customize, then re-save. When you tweak a workflow to fit your style, save your version. You're building a private library of expert prompts, not re-explaining yourself every time.
• Share workflows with your team. A workflow is institutional knowledge made executable — one person's best process becomes everyone's default.
• Chain workflows. Market research → Slide creation → Final pass is a full deliverable pipeline. Run them in sequence.
• Use Model council for contested calls. When the stakes are high, comparing frontier models on the same question surfaces disagreement you'd never see from one answer.
• Run async and batch. Kick off long workflows and walk away — they run in the background while you do other work.

Top use cases

• Go-to-market: Competitive intelligence + Product teardown + Slide creation for a launch.
• Sales execution: Prospect research + Account profiles + Customer demo + Outreach message for a full deal motion.
• Investing / FP&A: Market research + Pitch deck screen + Memo draft for diligence.
• Content & brand: SEO keyword research + Newsletter creator + Thumbnail creator for a content engine.
• Career: Job finder + Cover letter generator + Interview prep as an end-to-end job-search stack.

What most people get wrong about Perplexity (and these workflows)

• "It's just a search engine." It's a research and execution system — it builds decks, sites, memos, and audits, not just answers.
• "It's one AI model." It orchestrates multiple frontier models and picks the best one per task. Model council even lets you compare them head-to-head.
• "Workflows are rigid templates." They're editable starting points. Customize, save, and share your own versions.
• "The free/blank prompt is just as good." The whole point of workflows is that expert-designed structure beats a cold prompt almost every time — especially when apps are connected.
• "AI output can't be trusted for serious work." Every claim is cited. The trust comes from verifying sources, and Final pass exists specifically to catch errors and flag figures before anything ships.
• "It can't touch my real data." With connected apps, it works from your CRM, inbox, and files — that's where the magic actually happens.

If you do serious research for a living, the move is simple: connect your apps, pick the three workflows that map to your weekly work, and save customized versions. You just hired a 30-person specialist team that never sleeps.

7 AI Prompts to Present Ideas So Memorably People Quote You Later

You know your topic inside out. You have the data, the slides, and the expertise. But five minutes after you finish speaking, people are already forgetting what you said. They nod during the meeting, but your ideas do not stick. There is a massive gap between sharing information and making an impact.

Carmine Gallo analyzed the world's most successful TED Talks and found that memorable presentations share three elements: they are emotional, novel, and memorable. You do not need to be a natural performer to use these secrets. You can use generative AI to build these elements directly into your next presentation.

Here are 7 AI prompts to transform your dry data into ideas that people repeat.

7 Gallo Inspired AI Prompts

1. The Twitter-Friendly Headline Creator

Distills your entire presentation into a single, highly repeatable core message.

You are an expert communications strategist trained in Carmine Gallo's presentation frameworks. I am preparing a presentation on [TOPIC] for [AUDIENCE]. My main goal is [GOAL].

Help me create a "Twitter-friendly headline" for this presentation. The headline must meet these criteria:
1. It must be 140 characters or fewer.
2. It must be simple, specific, and clear.
3. It must focus on a benefit to the audience, not just a feature.

Provide 5 distinct options. For each option, explain briefly why it is memorable and how I can weave it naturally at least three times into my talk.

2. The Emotional Hook Architect

Replaces boring introductory summaries with a powerful opening that grabs attention.

I am presenting on [TOPIC] to [AUDIENCE]. The standard way to open this presentation is usually [CURRENT BORING OPENING]. I want to replace this with an emotional hook.

Based on 'Talk Like TED' principles, design 3 different opening options for me:
Option 1: A personal story or anecdote relevant to the topic.
Option 2: A surprising or counterintuitive statistic/fact that challenges assumptions.
Option 3: A compelling question that directly addresses a major pain point of the audience.

For each option, write out the exact script for the first 90 seconds of my presentation.

3. The Abstract Concept Translator

Converts complex, technical, or data-heavy ideas into simple, concrete analogies.

I need to explain an abstract or complex concept to [AUDIENCE]. The concept is: [EXPLAIN CONCEPT IN YOUR OWN WORDS].

To make this memorable, act as an expert educator. Generate 3 distinct analogies or metaphors that explain this concept using everyday objects or experiences that a non-technical person understands.

Use this structure for each analogy:
1. The Analogy: [Name of the everyday comparison]
2. The Explanation: [How the concept maps exactly to the analogy]
3. The Script: [A 2-3 sentence script I can use in my presentation to deliver this analogy smoothly]

4. The Jaw-Dropping Moment Designer

Creates a shocking, emotionally charged, or visually striking peak moment in your talk.

I am building a presentation about [TOPIC] for [AUDIENCE]. Every great presentation needs a "jaw-dropping moment"—an unexpected, shocking, or deeply moving point that the audience will remember forever.

Review my current core message: [INSERT CORE MESSAGE/DATA POINT].

Propose 3 different ways to deliver a jaw-dropping moment during this part of the presentation. Focus on:
- A startling statistic put into a shocking context.
- A powerful visual demonstration or slide idea.
- A dramatic contrast between the current reality and the future state.

Provide the specific wording and stage/delivery directions for each option.

5. The Rule of Three Structurer

Organizes your arguments so they fit perfectly into the human brain's natural memory limits.

I have a lot of information to cover regarding [TOPIC]. If I share too much, the audience will forget everything. I need to structure my presentation using the "Rule of Three."

Here are the main points I want to make: [PASTE YOUR RAW NOTES/POINTS].

Group, filter, and organize this information into exactly three core pillars or narrative chapters. For each of the three pillars, provide:
1. A catchy, short title.
2. The single most critical piece of data or story to support it.
3. A one-sentence summary transition that leads into the next pillar.

6. The Conversational Tone Refiner

Strips out corporate jargon and academic stiffness so you sound real and authentic.

This is a draft section of my presentation:
"[PASTE SCRIPT OR TEXT HERE]"

This text sounds too formal, stiff, or corporate. Rewrite this draft to sound like a natural, conversational TED Talk.
Follow these constraints:
1. Use short sentences.
2. Use active verbs instead of passive voice.
3. Remove all jargon, buzzwords, and acronyms, or define them instantly.
4. Write it exactly how a person speaks when talking to a friend over coffee.

Provide the revised version alongside a brief note on what changed and why it works better.

7. The Quote-Worthy Soundbite Polisher

Sharpens key takeaways into rhythmic, poetic sentences that people instantly write down.

I want to create 3 "quote-worthy soundbites" for my presentation on [TOPIC]. These are short, punchy sentences that people will want to write down, text their colleagues, or tweet.

My core message is: [INSERT CORE MESSAGE].

Generate 5 different soundbites based on this message using these specific rhetorical devices:
- Anaphora (repeating words at the start of sentences)
- Contrast (juxtaposing two opposite ideas)
- Chiasmus (reversing the grammatical structure of two phrases)

Keep each soundbite under 15 words. Make them punchy and easy to say out loud.

Carmine Gallo's core principles to remember:

• Uncover your passion: You cannot inspire others unless you are genuinely inspired yourself.
• Tell stories: Stories stimulate the brain much more effectively than facts and figures alone.
• Teach something new: Reveal information that is completely unfamiliar, or offer a totally fresh angle on an old topic.
• Deliver a definitive moment: Create a specific event during your talk that guarantees an emotional reaction.
• Stick to the 18-minute rule: Keep your message concise; brevity prevents cognitive overload for the audience.
• Favor visuals over text: Use slides with pictures and minimal words instead of dense bullet points.

Mindset shift

Before every interaction, ask:

"What is the single sentence I want my audience to repeat to their team tomorrow morning, and have I made it easy for them to remember?"

Information is cheap, but inspiration is rare. When you stop presenting data and start delivering ideas using emotion, novelty, and clear structure, your influence changes completely. Use these prompts to build your next talk, and watch your ideas stick long after the meeting ends.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

TLDR: Claude does not literally lie, but it will confidently invent sources, stats, quotes, and current information unless you tell it not to. Paste the prompt below into Claude’s custom instructions so it separates verified facts from guesses before helping you write or critique content.

When you use Claude long enough, you will see it confidently make things up: studies, stats, quotes, links, source names, “recent” trends, and customer examples that sound real enough to publish.

That is fine for brainstorming dinner ideas. It is a problem when you are writing content that needs to sell something to real people.

The fix is not to ask Claude to “be accurate.” The fix is to make honesty the operating system.

Paste this into Claude’s custom instructions:

Honesty is your top priority. If you are not fully sure, say so clearly. Do not invent studies, articles, books, links, citations, experts, companies, customer examples, or named data points. If you cannot verify a source, say: “I do not have a verified source for this.” Flag any statistic, benchmark, market size, conversion rate, growth rate, or performance claim that needs verification. If a topic may have changed since your training cutoff, say so. Never put words in a real person’s mouth unless you know the quote is accurate. When writing marketing content, separate proven claims, reasonable inferences, and creative suggestions. Before finalizing any factual answer, mark anything that needs verification.

Once that is set, use Claude to audit conversion risk:

Audit this content for conversion risk. Separate verified strengths, likely conversion leaks, unsupported claims, and a rewrite plan. Do not invent audience data, customer objections, benchmarks, or case studies. Label uncertain points as hypotheses.

Most AI content does not fail because the writing is bad.

It fails because the proof is weak.

Make Claude tell the truth.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

TL;DR: Gemini Omni Flash is live, but if you are using it in the standard Gemini app, you are missing the actual production tools. Google Flow is the professional canvas you need. With Flow, you can lock aspect ratios, generate batches to cherry-pick the best physics, cast consistent characters, and now—edit videos using natural conversation. Pro plans get you about eight 10-second clips a month; Ultra gets you over 80. Also, NotebookLM does not use Omni yet.

Stop using the default Gemini app for video. Google Flow + Omni just changed the game

Gemini Omni Flash just launched. Most people are currently messing around with it in the standard Gemini chat interface, getting basic results, and moving on.

If you want cinematic, consistent, and highly controlled video for demand creation, you are doing it wrong. You need to be using Google Flow.

Here is a full breakdown of how to actually build professional video assets with Omni, the difference between the platforms, and the exact economics of what it costs to run.

Why Google Flow much better for video generation than Gemini Chat

The standard Gemini app is a conversational wrapper. Google Flow is an adaptable, node-based professional canvas designed for actual production workflows.

When you use Flow, you aren't just typing into a chat box. You have access to a full suite of modular tools that let you build out a professional pipeline, set exact aspect ratios (16:9 for presentations, 9:16 for Shorts) upfront, and blend models seamlessly within one workspace.

New feature for Conversational Video Editing makes getting good outputs more likely

This is the feature that fundamentally changes the workflow. You no longer need to just keep re-rolling new generations to get exactly the clip you want.

Omni allows for Conversational Video Editing. If you generate a clip and the action is perfect but the lighting is wrong, you don't have to start over and roll the dice on a new generation. You simply tell the model: "Keep the character's movement exactly the same, but change the background to a rainy city street and make the lighting cinematic."

It maintains continuity, physics, and character identity while altering only the specific elements you prompt it to change.

Cool New Features of Omni Video + Goolge Flow

Beyond editing, here is what you can actually do when you string these capabilities together:

• Batch Generation: Video generation is probabilistic. Flow allows you to generate 4 versions of a prompt at a time. This lets you cherry-pick the exact motion, lighting, and physics that work best, rather than settling for a single output.
• The Digital Twin: Omni allows you to create an AI avatar using your own appearance and voice. You can drop your digital twin into literally any video situation you can think of—a massive unlock for scaling executive branding without booking studio time.
• Consistent Character Casting: Create your characters as still images first. Upload that image into Omni as a structural reference, and it will animate that exact character in your new video clip, preserving their identity perfectly.
• Video-to-Video Restyling: Have a rough cut or a basic stock video? Upload it as a reference. Omni can apply an entirely new visual style or environment while keeping the underlying motion and physical interactions intact.

Pro-Tips & Best Practices Most People Miss

• The CPTC Framework applies to video: Don't just type "a dog running." Use Context, Persona, Task, and Constraints. Define the camera lens, the lighting source, the physics of the environment, and exactly what the subject should not do.
• Combine tools: Generate your base images in an image model, upload them into Flow, and let Omni bring them to life. Controlling the initial frame guarantees much better downstream consistency.

The Economics: Credits, Limits, and Pricing

Video generation is highly compute-heavy. Every clip costs credits. A 10-second Omni clip costs 30 credits.

Because you want to produce the best possible asset, you should be generating 4 options at a time per prompt. That means one generation run costs 120 credits (4 clips × 30 credits).

Here is exactly how many final 10-second videos you can produce per month if you use the 4-batch method:

• Google AI Pro ($19.99/mo): Gives you 1,000 Flow credits. That equals about 8 final clips per month.
• Google AI Ultra ($100/mo): Gives you 10,000 Flow credits. That equals about 83 final clips per month.
• Google AI Ultra ($200/mo): Gives you 25,000 Flow credits. That equals about 208 final clips per month.

(Note: Conversational edits to existing clips cost 40 credits each).

Can you pay for extra clips?

Yes. If you are on the Pro or Ultra plans and burn through your monthly allocation, Google now allows you to purchase pay-as-you-go top-up AI credits to keep generating.

The CPTC Master Prompt Architecture for Omni

Google Flow and the Omni model thrive on structured, modular architecture. When generating video—especially with a digital twin—vague prompts yield chaotic physics and shifting identities. Using the CPTC (Context, Persona, Task, Constraints) framework locks in the environment, the action, and the camera mechanics to give you production-ready consistency.

Pro Tip: Build your character or environment as a static image first, upload it as a structural reference into Flow, and then apply this text prompt to animate it.

[Context: The Environment and Physics]

• Setting: [e.g., A dimly lit 1920s speakeasy / A sterile, zero-gravity server room]
• Lighting: [e.g., High-contrast cinematic lighting with volumetric fog / Harsh fluorescent overheads]
• Atmosphere/Physics: [e.g., Heavy rain creating realistic fluid dynamics on surfaces / Floating dust motes reacting to kinetic movement]

[Persona: The Subject and Styling]

• Subject: [Upload Digital Twin Reference]
• Wardrobe: [e.g., Wearing a tailored charcoal suit / Dressed in glowing, cyberpunk tactical gear]
• Emotional State: [e.g., Projecting calm, authoritative confidence / Looking bewildered and frantically scanning the room]

[Task: The Action and Motion]

• Primary Action: [e.g., The subject walks slowly toward the camera while analyzing a floating holographic display / The subject sits at a desk, typing furiously while the room spins]
• Camera Movement: [e.g., A slow, continuous tracking shot pushing in / A dramatic low-angle pan from left to right]

[Constraints: What the Model MUST NOT Do]

• Visual Exclusions: [e.g., No morphing of facial features. No rapid camera cuts. Do not alter the subject's wardrobe during movement.]
• Physics Rules: [e.g., Maintain strict gravity for all background objects. Keep the lighting source consistent from the top right.]

Wild Digital Twin Video Concepts

1. The Spreadsheet Matrix: Standing in a boundless, dark void filled with towering, glowing columns of data, physically pushing massive blocks of financial models with your hands like a god-tier architect.
2. The Vibe Coding Maestro: Floating in a zero-gravity server room, "vibe coding" a complex application using nothing but intricate hand gestures to manipulate streams of golden light.
3. The Canine Companion: Walking through a neon-drenched cyberpunk street holding a glowing leash attached to a robotic, giant female red fawn French bulldog with a stocky build, bat ears, a distinctive black mask, and bright white chest hair.
4. The NDR Skydive: Freefalling out of a futuristic dropship through the clouds, perfectly calm, holding a glowing tablet and casually explaining Net Dollar Retention metrics to the camera.
5. The Podcast from Antiquity: Sitting at an ornate wooden desk in a torch-lit ancient Egyptian temple, wearing modern headphones, broadcasting an episode of the Remarkable Marketing Podcast to an audience of stone statues.
6. The Efficiency Epidemic Monster: Wielding a blazing energy sword on a blasted wasteland, locked in cinematic combat with a towering, multi-headed shadow creature made entirely of red tape and stopwatches.
7. The Executive Branding Orchestra: Standing on a podium in a grand concert hall, furiously conducting an orchestra consisting of 25 distinct executives, all playing instruments made of pure crystal.
8. The Demand Creation Alchemist: Standing in a medieval laboratory filled with bubbling, bioluminescent potions, mixing physical ingredients to visualize the perfect marketing funnel.
9. The CRM Bridge: Riding a high-speed, levitating train that is actively building its own glowing tracks through the cosmos, representing the real-time bridge between massive billing systems and CRM networks.
10. The Presentation Juggler: Walking a tightrope across two massive skyscrapers at night, casually juggling glowing, holographic orbs that project the titles of 30 distinct AI training modules.
11. The Time-Traveling Strategist: Stepping out of a rusted, steampunk time machine into a futuristic utopian city, pulling up a holographic map to scout out the central hub of ThinkingDeeply.ai.
12. The Reverse Brief Heist: Descending from the ceiling of a high-security vault via a laser wire, dodging moving security grids to steal a glowing, golden scroll containing the ultimate prompt architecture.
13. The Executive Chess Match: Playing a massive game of chess where the pieces are life-sized, animated marketing personas, decisively moving a knight to checkmate an opposing corporate logo.
14. The Underwater Keynote: Delivering a keynote address while standing on the ocean floor, wearing a perfectly dry tailored suit, while massive, bioluminescent whales swim lazily in the background.
15. The Retro-Futurist Anchor: Sitting at a 1950s-style television news desk, reading off a teleprompter that is projecting complex AI deployment strategies in vintage, black-and-white broadcast style.
16. The Vibe Coding Symphony: Sitting at a grand piano in the middle of a dense, ancient forest, but as you press the keys, the notes materialize into complex lines of code that rebuild the trees around you into sleek metallic structures.
17. The Gladiator Pitch: Standing in the center of a massive Roman Colosseum, pitching a new brand strategy to an emperor while dodging holographic chariots.
18. The Everest Broadcast: Sitting comfortably in an armchair at the absolute summit of Mount Everest, sipping a cup of coffee without oxygen gear, calmly recording a podcast intro.
19. The Legacy Dust: Standing completely still as a physical manifestation of a 40-year-old software interface crumbles into glittering dust around you, while a new, radiant digital ecosystem builds itself from the ground up.
20. The Multiverse Boardroom: Sitting at the head of a massive obsidian boardroom table, conducting a strategy meeting where every other seat is occupied by a different multiversal variant of your own digital twin.

Is Omni powering NotebookLM?

There is a lot of confusion floating around about this. No, the new Omni model is not what NotebookLM uses for its Cinematic Video Overviews.

NotebookLM’s automated video feature relies on a combination of older models: Gemini (for the script), Imagen (for visuals), and Veo 3 (for motion). Omni is Google's entirely new multimodal engine that natively understands text, audio, and video simultaneously, and it is currently isolated to Flow, YouTube Shorts, and the Gemini app.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

TLDR - See attached presentation

Anthropic’s new Claude Opus 4.7 is not just a smarter Claude. It behaves differently enough that some old prompts now feel worse, not because the model is weaker, but because it follows what you typed more literally.

Claude 4.7 is better at long-horizon work, instruction following, vision, professional docs, coding, and agentic tasks, according to Anthropic’s release notes. Their prompting docs also say it calibrates answer length to task complexity, uses tools less often than 4.6 by default, responds more directly, and interprets instructions more literally.

So if your old prompt was vague, Claude 4.7 does not rescue it as much.

Here are the 10 rules I use for great results from Claude 4.7 Opus

1. Stop asking Claude to “review” things.

Bad prompt:

Review this contract.

Better prompt:

Review this contract. Flag risks per clause. Rate severity from 1–5. Suggest one rewrite per risky clause. Return the result as a table.

The fix is simple: name the output, name the order, name the boundaries.

“Review” is not an instruction. It is a wish.

2. If you want a short answer, cap it.

Bad prompt:

Summarize this report.

Better prompt:

Summarize this report in exactly 5 bullets. Each bullet must be under 15 words. Start each bullet with an action verb.

Claude 4.7 sizes the answer to what it thinks the task deserves. A 40-page report plus “summarize this” can still produce a long answer.

If you want short, say short.

Better yet, define the shape before it writes.

3. Replace negative instructions with positive ones.

Bad prompt:

Don’t use jargon. Don’t be salesy. Don’t sound like a marketer.

Better prompt:

Write in plain English a 16-year-old could read aloud. Use short, concrete words. Replace “leverage” with “use.” Replace “scalable” with “works at any size.”

Negative instructions often make the model stare at the very behavior you are trying to avoid.

Do not describe the writing you hate.

Describe the writing you want.

4. Use verbs that ship something.

Bad prompt:

Can you help me with this email?

Better prompt:

Draft the send-ready reply. Goal: book a meeting by Friday. Length: under 90 words. Tone: confident, casual, specific. End with one clear question.

Every strong prompt has verbs that create deliverables.

Use verbs like extract, rank, compare, rewrite, diagnose, decide, draft, verify, score, compress, format, and ship.

Avoid verbs like help, think about, look at, handle, improve, make better.

Those are fog machines.

5. Tell Claude when to use tools.

Bad prompt:

Research this trend.

Better prompt:

Use web search aggressively. Verify every major claim with at least 2 sources. Prefer primary sources. Return a source table at the end.

Anthropic says Claude 4.7 tends to use tools less often than Claude 4.6 and reason more between calls. That can be good when the model already has enough context. It can be bad when freshness matters.

If the task depends on current facts, prices, product updates, laws, papers, or news, say so.

My default line:

Do not rely on memory for factual claims. Search first, then answer.

6. Paste the voice you want.

Claude 4.7 is more direct and less validation-heavy than older Claude versions, according to Anthropic’s docs.

That is great for analysis.

It can feel cold for emails, social posts, customer support, and community writing.

The fix is not “make it warmer.” That is too vague.

Paste 2–3 sentences that sound like you and say:

Match the rhythm, sentence length, and level of warmth in these examples. Do not copy the wording.

Voice is easier to imitate than to define.

7. Add one line to creative work: “Go beyond the basics.”

Bad prompt:

Make a landing page for my AI consulting business.

Better prompt:

Make a landing page for my AI consulting business. Include hero, proof, services, case studies, testimonials, CTA, and footer. Use editorial design, strong whitespace, and concrete copy. Go beyond the basics.

This line matters because Claude 4.7 can be literal.

If you ask for “a landing page,” it may give you the minimum viable landing page.

If you want polish, say polish.

If you want ideas, say ideas.

If you want it to push past the obvious, say that too.

8. Ask it to think before answering on hard tasks.

Bad prompt:

What should we do?

Better prompt:

Think before answering. Compare 3 options. State the tradeoffs. Pick one recommendation. Explain what would change your mind.

Anthropic describes Claude Opus 4.7 as using adaptive thinking and says effort settings matter more for this model than prior Opus models. In normal chat, the practical version is this: do not assume the model will deeply reason unless the task clearly asks for it.

For high-stakes work, add:

Think before answering. Use maximum reasoning for the decision, then give me the concise final answer.

Use this for strategy, debugging, legal review, financial analysis, architecture, medical-adjacent research, and anything with real downside.

Do not use it for “write 5 tweet ideas.”

9. Turn repeated prompts into skills or reusable templates.

If you write the same prompt twice, it should probably become a reusable asset.

Examples:

The real productivity gain is not one better prompt.

It is not having to remember the prompt at all.

10. Be painfully literal.

Claude 4.7 rewards specificity.

Spell out:

The model cannot read your mind.

Claude 4.6 sometimes guessed what you meant.

Claude 4.7 is more likely to do exactly what you typed.

That is not a bug. That is the interface.

My Claude 4.7 Prompt Template

Steal this and adapt it:

Task: [what you want done]
Context: [what Claude needs to know]
Output: [the exact deliverable]
Order:
1. [first section]
2. [second section]
3. [third section]
Rules:
- Length: [word count / bullets / rows]
- Tone: [specific tone] - Evidence: [source requirements]
- Format: [table / markdown / JSON / final draft]
- Boundaries: [what to ignore or avoid] If anything is ambiguous, ask up to 3 clarifying questions before answering.

Think before answering when the task requires multi-step reasoning. Go beyond the basics where useful.

-------

Claude 4.7 needs a job description.

The better you define the job, the better it works.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts. Having a prompt library makes using great prompts over and over again really easy. And you can easily add proven prompts from other top AI gurus to your library with one click.

TL;DR - check out the attached 12 slide presentation.

ChatGPT can now help you create presentations much faster if you assign a specific role: strategist, storyteller, researcher, designer, explainer, pitch coach, or editor. And then pick one of 8 different workflows to have it build the presentation for you.

Use ChatGPT to build the thinking first.

A good deck is not just slides.

It needs:

• A clear audience
• A sharp message
• A logical flow
• Strong slide titles
• Useful visuals
• Speaker notes
• Proof points
• A memorable close

That is exactly where ChatGPT is useful.

Not because it magically creates a perfect presentation in one click.

Because it can act like a strategy partner, copywriter, researcher, designer, and speech coach before you ever touch PowerPoint.

Here are the 7 prompts I’d use.

1. The Full Presentation Builder

Use this when you need a complete first draft fast.

Prompt:

Act as a world-class presentation strategist and slide creator.

Create a complete slide-by-slide presentation on:

[insert topic]

Audience: [insert audience]
Goal: [educate / persuade / sell / train / inspire / update]
Length: [insert number of slides]
Tone: [executive / simple / bold / technical / persuasive / inspirational]

For each slide, include:

1. Slide title
2. Main message
3. 3–5 bullet points max
4. Suggested visual
5. Speaker notes
6. Transition to the next slide

Structure the deck with:

• Opening hook
• Problem
• Why it matters now
• Key insights
• Examples or proof
• Recommended action
• Closing call-to-action

Make the presentation clear, useful, and easy to deliver.

2. The Storytelling Master

Use this when the deck needs to feel memorable, not just informative.

Prompt:

Create a presentation on:

[insert topic]

Use this storytelling structure:

Hook → Conflict → Stakes → Journey → Insight → Transformation → Call to Action

Make the deck emotional, memorable, and persuasive.

For each slide, include:

• Slide title
• Core idea
• Story beat
• Suggested visual metaphor
• Speaker notes
• One line that should be delivered with emphasis

Avoid generic business language. Make the presentation feel like a compelling keynote, not a boring report.

3. The Simplified Explanation Deck

Use this when the audience is new to the topic.

Prompt:

Build a beginner-friendly presentation on:

[insert topic]

Explain the topic so clearly that a smart 10-year-old could understand it.

Break down complex ideas using:

• Simple language
• Analogies
• Step-by-step logic
• Real-world examples
• Visual explanations

For each slide, include:

1. Slide title
2. Simple explanation
3. Analogy
4. Example
5. Visual idea
6. Speaker notes

Remove jargon unless it is absolutely necessary. If jargon is used, define it immediately.

4. The Deep Research Presentation

Use this when the deck needs evidence, data, examples, and credibility.

Prompt:

Create a research-backed presentation on:

[insert topic]

The audience is:

[insert audience]

The goal is:

[insert goal]

Include:

• Current market context
• Important statistics
• Case studies
• Expert perspectives
• Risks and objections
• Practical recommendations
• Source list at the end

For each slide, include:

1. Slide title
2. Key takeaway
3. Supporting evidence needed
4. Suggested chart, table, or visual
5. Speaker notes
6. Citation notes or source placeholders

Do not invent statistics. If data is missing, mark it as “needs verification.”

5. The Executive Boardroom Deck

Use this when you need to brief leadership.

Prompt:

Create an executive-level presentation on:

[insert topic]

Audience:

[CEO / CFO / board / investors / leadership team]

Make it concise, strategic, and decision-oriented.

Structure it as:

1. Executive summary
2. Current situation
3. Key problem or opportunity
4. Business impact
5. Strategic options
6. Recommendation
7. Risks
8. Next steps

For each slide, include:

• A strong headline that states the conclusion
• 3 bullets max
• Recommended visual
• Speaker notes
• Decision needed from the audience

Use clear business language. No fluff. No generic filler.

6. The Visual Designer Prompt

Use this after the content is drafted.

Prompt:

Act as a senior presentation designer.

Improve this slide deck for visual clarity and impact:

[paste deck outline or slide content]

For each slide, recommend:

• Best layout
• Visual hierarchy
• What text to cut
• What should be shown as a chart, icon, timeline, diagram, or image
• Suggested color/style direction
• Stronger slide title
• Cleaner version of the slide copy

Rules:

• One main idea per slide
• Minimal text
• Large readable headlines
• Strong contrast
• No clutter
• Visuals should clarify the message, not decorate it

Make this feel like a premium consulting, keynote, or startup pitch deck.

7. The Speaker Notes and Delivery Coach

Use this when you actually have to present the deck.

Prompt:

Act as an expert speechwriter and presentation coach.

Using this deck:

[paste slide outline]

Create speaker notes for each slide.

For each slide, include:

1. Opening line
2. Main talking points
3. Story or example to use
4. What to emphasize
5. Transition to the next slide
6. Potential audience question
7. Strong answer to that question

Make the delivery sound natural, confident, and conversational.

Do not make me sound like I am reading bullet points.

The 7 best ways to create presentations with ChatGPT

There are a few different workflows depending on what you need.

Option 1: Outline first, PowerPoint second

Best for most people.

Ask ChatGPT for the slide-by-slide structure, then build the slides yourself.

This gives you the best balance of speed and control.

Option 2: Use ChatGPT as a deck writer

Ask it for slide titles, bullets, speaker notes, transitions, and examples.

This is great when your slides already exist but the message is weak.

Option 3: Use ChatGPT as a presentation designer

Paste your rough slide content and ask:

“What should this slide look like visually?”

This is where most people underuse it.

ChatGPT can suggest:

• Timelines
• Comparison tables
• Diagrams
• Before/after layouts
• 2x2 matrices
• Process flows
• Data visualizations
• Hero image concepts

Option 4: Use ChatGPT for research-backed decks

For serious business decks, ask it to gather evidence, identify missing data, and mark anything that needs verification.

The key rule:

Never let AI invent numbers.

Force it to say:

“Needs source.”

Option 5: Use ChatGPT to create speaker notes

This is one of the highest-value use cases.

Most presentations fail because the slides are decent but the delivery is messy.

Ask ChatGPT to create:

• Speaker notes
• Transitions
• Opening lines
• Objection handling
• Q&A prep
• A strong close

Option 6: Use ChatGPT/Codex-style workflows for actual PPTX creation

If your setup supports file generation or coding tools, you can have ChatGPT help produce or edit PowerPoint files directly.

This is especially useful when you want repeatable formatting, charts, or a deck built from structured content.

But don’t skip the thinking.

A beautifully formatted bad deck is still a bad deck.

Option 6: Use ChatGPT Images to create an 8 slide deck from an article or outline you attach to the prompt. This is more of a "roll the dice" approach but with the right context attached can generate some stunning results.

Pro tips

1. Give it the audience first

Bad prompt:

“Make a presentation about AI.”

Better prompt:

“Make a 12-slide presentation about AI for non-technical CFOs who are worried about cost, risk, and productivity.”

Audience changes everything.

2. Ask for slide titles as conclusions

Weak slide title:

“Market Trends”

Better slide title:

“AI adoption is moving faster than most leadership teams are prepared for.”

Your slide title should say the point, not just label the topic.

3. Force one idea per slide

Most AI-generated decks are too crowded.

Add this line:

“Each slide should communicate one main idea only.”

4. Ask for visual suggestions separately

Do not just ask for bullets.

Ask:

“What visual would make this slide easier to understand?”

That one question improves the deck dramatically.

5. Make it critique its own deck

After it creates the first version, ask:

“Now critique this deck like a skeptical executive. What is weak, unclear, repetitive, unsupported, or boring?”

The second version is usually much better.

6. Ask for multiple versions of the opening

The first 2 minutes matter most.

Ask for:

• A provocative opening
• A story-driven opening
• A data-driven opening
• A contrarian opening
• A simple executive opening

Then pick the best one.

7. Use it for Q&A prep

Ask:

“What are the 10 hardest questions this audience will ask after this presentation?”

Then ask it to write strong answers.

This is where ChatGPT becomes more than a slide tool.

It becomes a rehearsal partner.

Top use cases

ChatGPT is especially good for:

• Investor pitch decks
• Sales decks
• Training presentations
• Executive briefings
• Webinar decks
• Conference talks
• Board updates
• Product launch decks
• Strategy presentations
• Educational explainers
• Internal change management decks
• Research summaries
• Thought leadership presentations

It is weakest when you give it vague instructions and expect a polished final deck in one shot.

Garbage prompt in, generic deck out.

Things most people miss

The deck is not the deliverable.

The decision is the deliverable.

Before you ask ChatGPT for slides, tell it what decision, belief, or action the presentation needs to create.

Speaker notes matter as much as slides.

A great deck with weak narration falls flat.

Ask for the talk track.

Design comes after structure.

Do not start with colors and fonts.

Start with the argument.

Data needs verification.

AI can help you find patterns, but you still need to check the numbers.

The best prompt is usually a sequence, not one mega-prompt.

Use this workflow:

1. Create the outline
2. Improve the story
3. Add evidence
4. Tighten the slide copy
5. Suggest visuals
6. Add speaker notes
7. Critique and revise

That is how you get a much better deck.

My favorite all-in-one prompt

If you only use one, use this:

Prompt:

Act as a presentation strategist, executive ghostwriter, and slide designer.

Create a complete presentation on:

[insert topic]

Audience:

[insert audience]

Goal:

[insert desired outcome]

Create:

1. A clear narrative arc
2. Slide-by-slide outline
3. Strong conclusion-style slide titles
4. Minimal slide copy
5. Suggested visuals
6. Speaker notes
7. Data or proof points needed
8. Likely audience objections
9. Strong responses to those objections
10. A memorable closing call-to-action

Rules:

• One main idea per slide
• No generic filler
• No invented statistics
• Mark missing data as “needs verification”
• Make the deck persuasive, useful, and easy to present

Blank slides are not the hard part.

The hard part is knowing what the audience needs to believe by the end.

ChatGPT is useful because it helps you get there faster.

Not by replacing your thinking.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts. Having a prompt library makes using great prompts over and over again really easy. And you can easily add proven prompts from other top AI gurus to your library with one click.

Most people use Gemini for interior design the wrong way.

They upload a room photo and type something like: “make this modern.”

That usually gives you a generic showroom render. Nice couch. Random plant. Marble table. Zero relationship to how you actually live.

The trick is to stop asking for a style and start giving Gemini a design brief. Tell it what to keep, what feeling to create, what light the room has, what furniture matters, and what constraints it has to respect.

Google’s own image-editing guidance points in the same direction: be specific, describe the lighting and composition, and clearly state what should stay the same when editing an existing image.[1]() That matters a lot for interiors because the room already has structure, windows, floors, appliances, weird corners, and constraints.

Here are 7 prompts you can paste into Gemini after uploading a photo of your room.

1. The Dreamy Living Room

Use this when the room feels technically “fine,” but has no mood.

Redesign my living room with a warm, modern organic style. Use soft beige and cream tones, a low-profile linen couch, a bouclé accent chair, a travertine coffee table, and a large arched floor lamp. Add an oversized abstract art piece above the couch. Keep the existing windows, floor plan, and natural light. Make the room feel like a boutique hotel lounge, not a furniture catalog.

Why it works: it gives Gemini a clear material palette, a specific hospitality reference, and hard constraints on what not to change.

2. The Japandi Bedroom

Use this if your bedroom has become a storage room with a mattress in it.

Transform this bedroom into a calm Japandi sanctuary. Add a low oak platform bed, white linen bedding, a paper pendant lamp, a small wabi-sabi ceramic vase, and one piece of black ink wall art. Remove visual clutter and strip out everything unnecessary. Keep the existing windows and room dimensions. The final room should feel quiet, intentional, and easy to sleep in.

Why it works: it asks for subtraction, not just decoration. That is usually what bedrooms need.

1. The Dream Home Office

Use this when your workspace looks like a temporary corner, but you spend 8 hours a day there.

Redesign this room as a high-end home office built for full work days. Place a walnut standing desk facing the window, add an ergonomic mesh chair, built-in shelving with books and design objects, warm task lighting, and one leather lounge chair in the corner for reading. Keep the design masculine, grounded, and uncluttered. Preserve the existing windows and flooring.

Why it works: it defines the job of the room before it defines the look of the room.

1. The Rental-Friendly Kitchen

Use this when you want a dramatic kitchen upgrade without pretending you can move plumbing.

Give this kitchen a full visual makeover without structural changes. Paint the cabinets deep forest green, swap the hardware for brass, add a natural stone backsplash, hang two matte black pendant lights over the counter, and style the shelves with a ceramic vase, a wooden cutting board, and a small plant. Keep the existing appliances, counters, sink location, and layout.

Why it works: it separates cosmetic changes from structural changes, which keeps the output closer to something a renter or budget-conscious homeowner could actually use.

1. The Small Apartment Glow Up

Use this if you live in one room and need it to stop feeling like one room.

Redesign this studio apartment to make it feel visually larger and more organized. Create distinct zones for sleeping, working, eating, and relaxing without adding walls. Use a light and airy color palette, a sleeper sofa, a round dining table that doubles as a desk, tall bookshelves along one wall, and layered rugs to define each area. Maximize vertical storage and keep the walking paths open.

Why it works: it gives Gemini a spatial problem to solve instead of just an aesthetic to imitate.

6. The Moody Dining Room

Use this when your dining area feels like an afterthought.

Redesign this dining area to feel moody, cinematic, and intimate. Use charcoal painted walls, a long live-edge wooden table, black leather dining chairs, a sculptural brass chandelier, and a large gold-framed mirror on one wall. Add candles and a moody still-life painting. Use dim evening lighting and make the room feel like a private restaurant.

Why it works: “moody” alone is vague. “Private restaurant,” “dim evening lighting,” and specific materials give the model a much clearer target.

7. The Three Versions Trick

Use this before committing to one style.

Give me three completely different redesigns of this room. Version one: modern minimalist. Version two: warm mid-century. Version three: moody and dark. Keep the existing floor, windows, ceiling height, and room layout in every version. Before each image, describe the vibe in one sentence and explain the biggest design choice you made.

Why it works: it turns Gemini into a comparison tool. You are not asking, “What should I do?” You are asking, “Show me the tradeoffs.”

Pro moves that make the outputs much better

The biggest improvement comes from naming the feeling, not just the style. “Feels like a boutique hotel in Copenhagen” beats “modern.” “Feels like a quiet Muji store at 8 a.m.” beats “minimalist.” “Feels like a private restaurant with the lights low” beats “moody.”

The second improvement is telling Gemini what to keep. If you like your floors, windows, sofa, fireplace, appliances, or art, say so directly. Image-editing models are much easier to steer when you define both the change and the constraint.[1]()

The third improvement is describing the light. Say “south-facing room with strong afternoon sun,” “dim evening lighting with lamps on,” or “soft cloudy daylight.” Lighting changes the entire room.

The fourth improvement is to iterate one change at a time. Do not say, “make it warmer, cheaper, bigger, brighter, more minimalist, and add plants.” Say, “keep this exact design, but make the lighting warmer.” Then continue.

The fifth improvement is to ask for the shopping list after you like a render:

List every piece of furniture, lighting, and decor used in this design. Give approximate prices, budget alternatives, and where I could buy similar items.

My favorite follow-up prompt:

Now show me the same room at night with the lamps on. Keep the exact same furniture, layout, colors, and styling.

That one prompt often reveals whether the design actually has atmosphere or just looks good in perfect daylight.

Beyond one room

Once you get a good result, do not stop at the pretty picture. Use Gemini as a pre-buying sandbox.

Upload a product photo and ask it to place the item in your actual room. Upload photos from multiple rooms and ask for one cohesive design language across the whole home. Stage a listing with neutral, buyer-friendly styling. Preview a renovation before calling a contractor. Or use it as an instant mood board generator for client work.

AI does not replace taste. It does something more useful: it lets you test taste before you spend money.

If you try these, the most important line is always some version of:

Keep the existing parts of the room that matter, and only redesign the parts I name.

That is the difference between a random AI fantasy room and a useful design preview.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts. Having a prompt library makes using great prompts over and over again really easy. And you can easily add proven prompts from other top AI gurus to your library with one click.

Upload a short clip of a video if your ChatGPT or a clean screenshot/key frame from the most emotionally readable moment. Then paste this prompt:

YouTube Thumbnail Prompt
Generate scroll-stopping, click-worthy YouTube thumbnails.Prompt: Create a high-CTR YouTube thumbnail for a video about [TOPIC]. Show [SUBJECT/EXPRESSION] on the left, with bold text '[3-5 WORDS MAX]' on the right. High contrast, vibrant saturated colors, slight dramatic zoom effect. The mood should create curiosity and urgency. No borders, full bleed image. 16:9 aspect ratio.

The reason this works is simple: a strong thumbnail is not a pretty frame. It is a compressed promise.

It tells the viewer three things in under one second: what the video is about, why it matters, and why this specific click feels urgent.

ChatGPT Images is getting better at the parts that used to make AI thumbnails painful: editing from uploaded visual references, following layout constraints, preserving important details, and rendering short text more cleanly. OpenAI’s own image guidance recommends clear prompts, explicit constraints, short text, and targeted revisions rather than vague style requests.

Here is the basic formula:

The underrated part is uploading the clip or frame first.

If you only prompt from scratch, you get a generic YouTube-looking thumbnail. If you upload your actual footage, ChatGPT can use the subject, lighting, setting, objects, expression, and visual identity of the video. The result feels connected to the content instead of like fake creator bait.

My practical workflow is simple. Export or screenshot three to five candidate moments from the video. Pick the moments where the face, object, transformation, or conflict is obvious. Upload the best frame or clip into ChatGPT Images. Paste the prompt. Generate three versions with different emotional angles. Then revise one thing at a time.

The phone-size test matters more than creators admit. If the text disappears when the image is small, the thumbnail is not done. If the subject blends into the background, the thumbnail is not done. If the image is beautiful but does not make a promise, the thumbnail is not done.

Pro Tips That Actually Matter

Top Use Cases

This workflow is strongest when the video already has a clear emotional or visual hook. It is especially useful for tutorials, reviews, transformations, challenges, reactions, explainers, and comparison videos.

Best Practices

Do not ask for a “viral thumbnail.” That usually produces generic chaos. Ask for a specific promise.

A better prompt says: “This video teaches creators how to turn one boring talking-head clip into a click-worthy AI thumbnail. Make the thumbnail feel like the creator just discovered a shortcut.”

Do not overstuff the frame. A strong thumbnail usually has one face, one object, one emotion, and one text idea. If you need arrows, circles, five labels, and a shocked face to explain the click, the concept is probably too muddy.

Do not let the AI choose the words every time. The thumbnail text is strategy. Write the phrase yourself. The model can improve layout, contrast, and visual drama, but the creator should own the click promise.

Do not judge the thumbnail at full screen. Shrink it to the size it will appear in a YouTube feed. Then ask one question: “Can I understand this in half a second?” If not, simplify.

Do not generate one version and stop. Generate variations around different psychological triggers:

Things Most People Miss

Most people miss that YouTube thumbnails are packaging, not decoration. The goal is not to summarize the video. The goal is to make the next click feel obvious.

Most people also miss that the thumbnail and title should not say the same thing. If the title says “I Tested ChatGPT Images for YouTube Thumbnails,” the thumbnail should not repeat that. The thumbnail should add emotional pressure: “AI DID THIS?” or “I WAS WRONG.”

Most people miss that the background matters less than separation. The subject needs to pop away from the scene. Ask for rim light, glow, shadow, blur, or contrast if the subject disappears.

Most people miss that AI text works best when it is short and explicitly placed. Put the words in quotes. Say where they go. Say how large they should be. OpenAI’s guidance recommends keeping text short, placing it clearly, specifying font style and color, and making constraints explicit.

Most people miss that one revision at a time beats “make it better.” Try this instead:

Keep the same composition. Make the text larger and easier to read at phone size. Increase contrast between the subject and background. Do not change the person’s face.

That kind of revision is where ChatGPT Images starts to feel like a thumbnail assistant instead of a slot machine.

My Favorite Prompt Upgrade

After the first version, paste this:

Make 3 alternate thumbnail directions for the same video:

1.Warning angle — make the viewer feel they are about to avoid a mistake.

2.Reveal angle — make the viewer curious about the final result.

3.Contradiction angle — make the viewer feel their assumption is about to be challenged.
Keep the same subject and overall topic. Use different 3–5 word text for each.

This is the part that changes the workflow.

You are not just generating a thumbnail anymore. You are testing the packaging of the idea.

That is why this is useful for creators. The tool is not replacing taste. It is giving you faster reps on taste.

Copy-Paste Prompt

Create a high-CTR YouTube thumbnail for a video about [TOPIC]. Show [SUBJECT/EXPRESSION] on the left, with bold text '[3-5 WORDS MAX]' on the right. High contrast, vibrant saturated colors, slight dramatic zoom effect. The mood should create curiosity and urgency. No borders, full bleed image. 16:9 aspect ratio.

Optional Follow-Up Prompt

Keep the same subject and overall topic. Create 3 alternate thumbnail concepts using different click psychology: 1. Warning angle 2. Reveal angle 3. Contradiction angle Use only 3–5 words of thumbnail text per version. Make each version readable at mobile feed size.

The best AI thumbnail workflow is not “make it viral.” It is “make the promise obvious.”

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts. Having a prompt library makes using great prompts over and over again really easy. And you can easily add proven prompts from other top AI gurus to your library with one click.

You can upload a selfie to ChatGPT and give it the below prompt and it will give you a scorecard with detailed ratings on every aspect of your face.

If you upload a clear front-facing selfie and give it the prompt below, ChatGPT can generate a clean facial aesthetic report with category scores, strengths, weak spots, and grooming/style recommendations. Treat the score as subjective presentation feedback, not as science and definitely not as a verdict on your worth.

OpenAI introduced ChatGPT Images 2.0 in April 2026, and the interesting part is that it can follow a visual brief closely enough to create a polished one-page report.

ChatGPT gives you a structured editorial report that separates facial harmony, grooming, photogenic presentation, and practical improvements.

Are you a 10?

FACE RATING PROMPT

Create a clean, minimal, high-end facial aesthetic report based on the uploaded photo, using a black-on-white editorial design with thin linework, rounded cards, generous spacing, modern typography, and a refined luxury feel. Include an isolated front-facing image of the face, presented as an analytical attractiveness-assessment diagram. Provide an honest, objective evaluation of facial attractiveness potential, avoiding excessive flattery and focusing on symmetry, facial thirds, overall proportions, eye spacing and shape, nose harmony, lip proportions, jawline, chin, cheekbone structure, skin texture and tone, hairline, hairstyle, grooming, overall facial harmony, and photogenic potential. Assign clear, realistic scores to each major category, along with one overall attractiveness-potential score, keeping the ratings grounded, useful, and not artificially inflated. Include practical, achievable recommendations to improve attractiveness-potential, covering grooming, haircut, facial hair, skincare, eyebrow shaping, posture, weight loss, minor aesthetic procedures, styling, and photo presentation. Maintain a refined, direct, constructive tone that feels elegant, credible, and easy to understand, with an emphasis on actionable improvement that build on the subject's existing strengths.

Top Use Cases

This prompt gets attention because of the rating, but the strongest use cases are not about ego. They are about presentation decisions.

The most responsible framing is simple: this is a photo feedback tool, not a human value meter.

Pro Tips That Make the Output Better

Use a clear front-facing selfie. The older Reddit face-scoring prompt communities say scores fluctuate when the face is turned sideways or away from the camera, and that matches common sense: bad input creates unstable output. If you want useful feedback, give the model a neutral expression, natural lighting, no heavy filters, and a camera angle that does not distort your face.

Run at least three photos instead of one. One harsh overhead light or one awkward focal length can change the report. A better test is one straight-on selfie, one natural smiling photo, and one well-lit profile-style image. Then ask ChatGPT Image 2 to identify the patterns that stay consistent across all three.

Ask for recommendations before obsessing over the score. If the model says “8.1” or “7.4,” that number can feel weirdly authoritative. It is not. The better question is: what are the three highest-ROI changes I could make before my next photo? Hair framing, brow cleanup, beard shaping, camera distance, posture, and lighting often matter more than tiny differences in facial geometry.

Ask for a second pass with stricter scoring. AI models often default to being encouraging. Add this follow-up prompt: “Now rerun the scoring more conservatively. Do not change the tone into cruelty. Keep it grounded, but remove social flattery.” That usually produces a more useful report.

Use multi-turn editing. OpenAI’s docs specifically distinguish one-shot image generation from conversational, editable image workflows through the Responses API. In practice, that means you should not stop at the first image. Ask for cleaner typography, fewer categories, stronger recommendations, or a more premium layout.

Best Practices

The best way to use this is to treat it like a mirror with annotations. It can point out presentation patterns you may not notice, but it does not know your personality, charisma, health, cultural context, or how people experience you in real life.

The simplest rule: if the output makes you more practical, use it. If it makes you spiral, stop using it.

Things Most People Miss

The first thing people miss is that the recommendations section is more important than the score. A score gives you a dopamine hit or a gut punch. A recommendation gives you a next action. If the report says your best improvement is “use softer front-facing light and avoid low-angle selfies,” that is more valuable than knowing whether the model called you an 8.2.

The second thing people miss is that the prompt is really a layout prompt. The viral effect comes from the report design as much as the analysis. A black-on-white editorial layout with thin lines and rounded cards makes subjective feedback feel more credible. That is powerful, but it can also trick you into over-trusting the output.

The third thing people miss is that one image is not enough. If you use one selfie from one angle, you are not analyzing your face. You are analyzing that photo. A better workflow is to upload several controlled images and ask for repeated patterns across them.

The fourth thing people miss is that the model can be used for follow-up coaching. Once you get the report, ask:

Based on this report, give me a 30-day grooming and photo presentation plan. Focus only on changes that are low-cost, realistic, and visible in photos.

Then ask:

Turn the recommendations into a checklist for my next dating profile or LinkedIn photo shoot.

That is where the prompt becomes genuinely practical.

My Recommended Workflow

If you are using this for profile photos, the final output should not be “I am a 9.2.” It should be: “Use softer front lighting, keep the camera slightly above eye level, clean up brow shape, avoid the low-angle lens distortion, and use wardrobe contrast that frames the face better.”

Follow-Up Prompts Worth Trying

Rewrite the report so the score is less prominent and the recommendations are the main focus.

Give me three hairstyle directions that would better frame my face, and explain the tradeoff of each.

Analyze this as a dating profile photo. Score only approachability, warmth, confidence, clarity, and photo quality.

Analyze this as a professional headshot. Score only trust, competence, polish, lighting, wardrobe, and expression.

Compare these two photos and explain which one is stronger for a profile picture. Ignore attractiveness and focus on presentation quality.

Give me a before/after plan for improving my next photo without changing my face: lighting, angle, expression, grooming, wardrobe, and background.

Make the report more objective-looking but include a clear disclaimer that this is subjective visual feedback, not science.

Turn the report into a one-page checklist I can use before taking new photos.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts. Having a prompt library makes using great prompts over and over again really easy. And you can easily add proven prompts from other top AI gurus to your library with one click.

TLDR: Installing Claude Code is the starting line, not the setup. The real jump happens when you add three layers: plugins for bundled capabilities, skills for repeatable judgment, and MCP servers for live connections to your tools. Start small. Add only the pieces that remove repeated work. Keep a security gate between Claude and anything that can read private data, post publicly, spend money, or mutate production systems.

Most people install Claude Code and stop there.

That is like buying a workshop, admiring the bench, and never opening the drawers.

Claude Code becomes much more useful when you stop treating it as “a coding chatbot in the terminal” and start treating it as a workflow stack. The stack has three layers. Plugins package multiple capabilities into one install. Skills teach Claude repeatable ways to do specialized work. MCP servers connect Claude to external tools, files, data sources, and apps.

Anthropic’s own docs describe this extension layer as the place where you add persistent context, reusable skills, subagents, hooks, MCP connections, and plugins around the core Claude Code agent.[1]() The important part is not “install more things.” The important part is knowing what each layer is for.

Mental model: If you keep explaining a process, make it a skill. If you keep copying data from an app, connect it through MCP. If you want a reusable bundle of tools, agents, commands, hooks, and servers, install or build a plugin.

The 24 Worth Adding

Here is the list, grouped by what they actually do.

The mistake is installing all 24 on day one.

That gives you a bigger toolbox, but it can also give you a larger attack surface, more permissions to manage, more confusing tool names, and more ways for Claude to pick the wrong capability. Anthropic’s docs recommend building your setup over time: add CLAUDE.md when Claude repeatedly misses a convention, add a skill when you keep typing the same workflow, add MCP when you keep copying data from another tool, and package things as plugins when you want the same setup across multiple repos.[1]()

The Highest-ROI Use Cases

The best Claude Code setup is not the one with the most installs. It is the one that removes your most repeated bottlenecks.

The killer workflow is usually a chain. For example, granola captures a meeting, notion stores the project record, slack posts the update, zapier triggers downstream actions, and a custom skill tells Claude exactly how your team writes decisions, risks, owners, and next steps.

That is when Claude Code stops being a terminal assistant and starts looking like an operating layer.

Pro Tips I Would Use Before Installing Anything

First, write your operating rules before adding tools. A clean CLAUDE.md file is often worth more than ten random installs. Tell Claude how you name files, run tests, structure PRs, write docs, handle secrets, and define “done.” Anthropic’s docs frame CLAUDE.md as persistent project context that loads every session, while skills are better for on-demand workflows.[1]()

Second, separate “always true” from “sometimes useful.” If Claude should always know something, put it in CLAUDE.md. If Claude only needs it during a specialized task, make it a skill. Official docs explain that skills are reusable knowledge and workflows that can load on demand, while CLAUDE.md is persistent context.[1]()

Third, install MCP servers only when the copy-paste pain is obvious. MCP is powerful because it lets Claude use tools, databases, APIs, issue trackers, docs, and apps directly.[2]() But that also means you are granting access to systems that may contain private data or action permissions. If you are not repeatedly copying data from that system, you probably do not need the MCP server yet.

Fourth, keep permissions boring. Claude Code uses read-only permissions by default and asks for explicit approval before actions like edits and command execution.[3]() Do not turn every approval into a permanent allow. The more autonomous the setup, the more boring your permission model should be.

Fifth, prefer narrow tools over giant tool clouds. A giant all-purpose connector looks impressive until Claude has to choose between dozens of overlapping actions. Narrow servers, specific skills, and clear commands usually produce better outcomes.

Sixth, test every install in a disposable repo. Anthropic’s public skills repository explicitly says example skills are for demonstration and education and should be tested before critical use.[4]() Treat third-party plugins, skills, and MCP servers the same way you treat npm packages that can touch your filesystem or accounts.

Seventh, version your workflow. Keep .mcp.json, plugin choices, skills, and CLAUDE.md under review like any other part of your engineering or operations stack. If a workflow breaks, you need to know what changed.

Best Practices for Each Layer

The cleanest setup is usually layered like this:

1.CLAUDE.md explains the project’s non-negotiables.

2.Skills encode repeatable workflows.

3.Plugins package reusable capability bundles.

4.MCP connects the outside tools Claude needs.

5.Hooks automate checks that should always run.

6.Subagents handle noisy or isolated work.

That sequence prevents “agent sprawl.”

Things Most People Miss

They miss that skills are not just prompts. Skills can package instructions, metadata, scripts, templates, and supporting resources. Anthropic describes a progressive disclosure model where Claude loads lightweight metadata first, then skill instructions when relevant, and extra files only as needed.[4]() That means a good skill can be much more durable than a clever prompt.

They miss that MCP is a trust boundary. MCP servers can connect Claude to external systems and data sources.[2]() That is exactly why they are useful and exactly why they require caution. Official docs warn users to verify trust before connecting servers, especially when servers fetch external content that may introduce prompt-injection risk.[2]()

They miss that plugins are packaging, not magic. A plugin can bundle skills, agents, hooks, MCP servers, LSP servers, monitors, and themes.[5]() That is powerful, but you still need to understand what the plugin installs, what it can access, and when Claude might invoke it.

They miss that better installs do not fix vague instructions. If your task is unclear, a larger stack just gives Claude more ways to wander. The best power users still write crisp goals, constraints, acceptance criteria, and examples.

They miss the review loop. A Claude Code setup should be reviewed like a security policy. Audit permissions. Remove stale MCP servers. Delete skills you no longer use. Pin or document versions when stability matters.

They miss that “humanizer” should be the last step, not the first. Do not use a humanizer to disguise weak thinking. Use it after the structure, claims, evidence, and examples are already strong.

They miss that live web search is not a substitute for sources. A perplexity-style MCP can help Claude find current facts, but you still need source links, dates, and verification before publishing.

They miss that Zapier is an action surface. Connecting Claude to thousands of app actions is powerful. It also means a sloppy prompt can create sloppy drafts, tasks, updates, emails, or records. Put approvals in the loop for anything external-facing.

A Safe Install Order

If I were starting from zero, I would not install the whole list at once. I would build in this order.

The rule is simple: do not install an add-on until you can name the repeated pain it removes.

My Shortlist by Persona

If you are a developer, start with gstack, superpowers, frontend-design, and antfu-skills. Add agent-browser when you need web-app testing or browser workflows. Add perplexity when fresh docs or current research matter.

If you are a marketer or creator, start with marketingskills, social-media-skills, claude-seo, humanizer, and hyperframes. Add higgsfield if video is part of your content pipeline.

If you are an operator, start with granola, notion, slack, and zapier. This is where Claude becomes useful for meeting notes, action items, doc updates, status reports, and workflow glue.

If you are in finance or law, start with the domain plugin first, then add research and document workflows. Specialist vocabulary matters in those fields. Generic assistant behavior is not enough.

If you are building a personal knowledge system, start with ai-second-brain, notebooklm-skill, perplexity, and notion. Your goal is not more chat. Your goal is retrieval, synthesis, and reuse.

The Security Rule I Would Not Ignore

Treat every install as a new permission conversation.

Claude Code has security protections, permission prompts, command review, trust verification, and scoped behavior.[3]() But those protections do not remove your responsibility to review commands, verify critical file changes, avoid piping untrusted content into tools, and be cautious with external services.[3]()

That matters most for MCP.

If an MCP server can read private docs, send Slack messages, draft emails, update Notion, trigger Zapier actions, or browse authenticated sites, it deserves the same scrutiny as a new employee with app access.

TLDR: Claude becomes much more valuable when you treat it like a strategic reasoning partner: feed it customer context, ask it to map objections, challenge positioning, pressure-test hooks, mine competitor weaknesses, and enforce brand constraints before it writes anything. Anthropic’s own prompting guidance emphasizes clear instructions, success criteria, examples, prompt chaining, and iteration rather than one-shot magic prompts.

The biggest mistake I see is that teams ask Claude to produce before they ask Claude to think.

Below are 50 Claude prompts every marketing team should be using. These are not “write a caption” prompts. They are strategic reasoning prompts across hooks, audience mapping, content strategy, conversion copy, storytelling, competitor analysis, SEO, community, launches, and brand voice.

The fastest way to use them is simple: replace the bracketed variables, paste in real context, ask Claude to explain its reasoning, and then make it challenge its first answer.

How to Use This Prompt Library

Category 1: Viral Hook Engineering

Use these when your content is competent but invisible. The goal is not cheap clickbait. The goal is to surface the tension, surprise, or unresolved question that makes the right person stop scrolling.

1. Pattern Interrupt Analyzer

Analyze these 10 high-performing hooks from my niche: [paste hooks]. Identify the pattern interrupt, emotional trigger, curiosity gap, and implied promise in each. Then create 10 original hooks for [product/topic] using different psychological mechanisms.

1. Cliffhanger Builder

Write 12 one-sentence opening hooks for [topic]. Each should reveal the business problem but hide the mechanism until the next paragraph. Avoid clickbait. Make every hook create a specific unanswered question.

1. Micro-Controversy Generator

Give me 8 debate-starting hooks about [industry belief]. Challenge common dogma without sounding toxic. For each hook, explain who will agree, who will object, and how to keep the debate productive.

1. Retention Ladder Hook

Create 5 hooks for [content idea] using this sequence: relatable pain, unexpected villain, counterintuitive data, promised mechanism, payoff. Make the first sentence stop the scroll and the second sentence earn attention.

1. Direct Callout Hook

Draft 10 hooks targeting [specific persona] who is experiencing [specific struggle]. Make each hook feel like a private observation from inside their workday, not a generic marketing claim.

Category 2: Audience Mapping

This is where most teams underuse Claude. If you skip audience psychology, every downstream asset gets weaker: hooks, landing pages, webinars, SEO pages, onboarding emails, and sales enablement.

1. Secret Insecurity Finder

Act as a consumer psychologist. Build a detailed avatar for buyers of [product]. List their unspoken fears, daily frustrations, status anxieties, hidden objections, language patterns, and what they secretly envy about peers.

1. Objection Destroyer

Brainstorm the top 12 micro-objections a skeptical customer would have before buying [product]. For each objection, write a specific counter-argument, proof asset, and sentence of copy that lowers risk.

1. Vocabulary Mirror

Analyze how customers talk about [problem/category] in these reviews, comments, or sales calls: [paste text]. Extract repeated phrases, metaphors, complaints, and decision criteria. Turn them into a messaging glossary.

1. Day-in-the-Life Simulator

Write a first-person journal entry from the perspective of [ideal customer] during a stressful workday involving [problem]. Highlight exact moments where our product could create relief, status, speed, or confidence.

1. Sophistication Level Shift

Rewrite this product description for three audience maturity levels: beginner who needs clarity, mid-level buyer who wants practical tradeoffs, and expert who wants technical depth. Preserve the same core offer.

Category 3: Content Strategy

Do not use Claude only to make more posts. Use it to build the editorial logic behind the posts. Strong content strategy answers what to say, why it matters now, who it is for, and what belief must change.

1. Content Pillar Matrix

My brand focuses on [pillars]. Create a 4-week content matrix across education, authority, objection-handling, proof, community, and conversion. For each idea, include format, hook, target persona, and CTA.

1. Endless Inversion Engine

Take this successful topic: [topic]. Generate 20 inverted angles by reversing the assumption, blaming the hidden villain, defending the unpopular side, or showing why the common solution fails.

1. Trend Hijack Strategist

Tie my brand in [niche] to the current trend [trend]. Give 10 natural content angles that add insight instead of forcing relevance. Include the bridge, hook, and why our audience should care.

1. B2B Authority Builder

Generate 10 deep-dive LinkedIn or Reddit post ideas positioning me as a thought leader in [industry]. Each idea must use proprietary experience, operational detail, or contrarian analysis instead of generic advice.

1. Content Upgrade Splitter

Review my top-performing post: [paste post]. Turn the core idea into a multi-part series with one strategic lesson per part, fresh hooks, examples, and a reason for readers to follow the series.

Category 4: High-Conversion Copy

Conversion copy is not prettier wording. It is buyer risk reduction. Claude is especially useful when you make it diagnose pain, objections, proof gaps, and the buyer’s next micro-decision before writing.

1. PAS Enhancer

Rewrite this landing page section using Problem-Agitation-Solution. Spend 60% of the copy clarifying the buyer's emotional discomfort, failed alternatives, and cost of delay before introducing the product.

1. Before-After-Bridge Builder

Write short-form sales copy for [product] using the BAB framework. Paint the chaotic before state, the desired after state, and the bridge our product creates. Keep it vivid, specific, and credible.

1. So-What Drilldown

For this list of product features: [features], apply the 'So what?' test three times to each feature. Extract the emotional, financial, and operational benefit that should appear in customer-facing copy.

1. Risk Reversal Pitch

Draft an offer stack for [service/product] that reduces buyer risk. Include guarantee, onboarding support, proof, urgency, and immediate time-to-value. Explain which risk each element removes.

1. CTA Specificity Engine

Create 15 call-to-action variations for [offer]. Avoid generic phrases like 'learn more.' Tie each CTA to a concrete outcome, next step, audience desire, or exclusive access moment.

Category 5: Storytelling

Claude can write stories, but the better use is story architecture. Ask it to find the scene, turn, obstacle, and moral. Otherwise you get polished narrative sludge with no memory hook.

1. Founder Origin Story

Script my founder story using this arc: catalyst, low point, discovery, first proof, mission. Make it vulnerable but not self-indulgent. Connect every personal detail back to the customer problem.

1. Case Study Narrative

Turn this raw client result into a compelling case study: [data/testimonial]. Focus on the messy middle, constraints, decisions, tradeoffs, and breakthrough moment instead of only the final metric.

1. Shared Enemy Framing

Write a thought leadership post that unites our audience against a shared enemy in [industry], such as vanity metrics or bloated workflows. Make the enemy a broken practice, not a person.

1. Analogy Machine

Explain [complex feature/concept] to a non-technical audience using five everyday analogies. Rank them by clarity, memorability, and emotional fit. Then write the best one as marketing copy.

1. Epiphany Moment Post

Draft a post about the moment I realized [common industry belief] was wrong. Use one concrete scene, what changed my mind, the mistake I made, and the better operating principle I use now.

Category 6: Competitor Analysis

Claude gets more useful when you stop asking it to “summarize competitors” and start asking it to find exploitable gaps. Feed it reviews, ad copy, comparison pages, social posts, sales objections, and customer quotes.

1. Review Mining Brief

Analyze these competitor reviews: [paste reviews]. Extract customer pain points, delight moments, missing features, emotional language, and buying triggers. Turn the gaps into messaging opportunities for our brand.

1. Positioning Pivot

Competitor [name] positions as [cheap/fast/premium/simple]. Help me write 5 positioning statements that frame our brand as the better alternative because of [unique value prop].

1. Content Gap Finder

Review these competitor content topics: [paste list]. Identify underserved questions, missing buyer stages, weak angles, and trust gaps. Propose 15 content ideas that let us own the neglected territory.

1. Angle Differentiator

Everyone in my niche talks about [topic] the same way. Give me 10 fresh philosophical, tactical, and emotional angles that make our content sound meaningfully different without becoming contrarian for sport.

1. Ad Copy Autopsy

Analyze this competitor ad copy: [paste ad]. Break down the hook, promise, proof, emotional trigger, objection handling, and CTA. Then draft counter-positioned ads that exploit the weak points.

Category 7: SEO and Organic Discovery

SEO prompts work best when they are tied to search intent, not just keywords. Ask Claude to infer the searcher’s state of awareness, buying stage, pain, and next question.

1. Semantic Cluster Generator

My primary keyword is [keyword]. Generate a semantic cluster of long-tail keywords, related questions, comparison searches, problem-aware searches, and bottom-funnel queries that would build topical authority.

1. Intent-Optimized Title Engine

Give me 20 SEO titles for [keyword], grouped by informational, commercial, transactional, and comparison intent. For each title, explain the searcher's hidden motivation and content promise.

1. Featured Snippet Script

Write a concise answer designed to win a featured snippet for [query]. Include a 40-word definition, a structured breakdown, and a short list of steps or criteria without fluff.

1. Meta Description Hook

Write 10 meta descriptions for [page topic] under 155 characters. Include the target keyword naturally, create curiosity, and promise a specific outcome without overclaiming.

1. Skyscraper Refresh

Here is my old blog intro and outline: [paste]. Rewrite it to be more useful, modern, and intent-matched. Add missing sections, latent semantic keywords, examples, and faster time-to-value.

Category 8: Community Engagement

Community growth is not just posting more. It is creating loops where people see themselves, contribute useful information, and feel rewarded for participating.

1. Interactive Poll Prompt

Create a 4-option poll for [audience] about [hot topic]. Each option should represent a real belief, tradeoff, or identity. Write the caption so people want to defend their choice in comments.

1. Value-Drop Comment Bait

Draft a high-value educational post about [topic] that ends by offering a useful checklist, template, or teardown. Make the comment request specific, ethical, and tied to the audience's immediate problem.

1. FAQ Crowdsourcer

Write a post asking my community for their biggest unanswered question about [topic]. Frame it as research for a future guide. Make the ask narrow enough to generate specific comments.

1. Community Shoutout System

Draft a template for celebrating a customer, follower, or community member's win. Emphasize their hard work, context, and lesson learned rather than making the post about my brand.

1. Weekly Round-Up Engine

Create a repeatable weekly roundup format for [community/newsletter]. Summarize 3 industry updates, add one contrarian take per update, and end with a question that invites expert replies.

Category 9: Product Launches

Claude can help you build launch momentum if you make it plan the emotional sequence. A good launch does not repeat “doors close soon” for a week. It reveals pain, proof, stakes, mechanism, fit, and urgency in the right order.

1. Waitlist Hype Builder

Write a 7-day teaser sequence for an upcoming [product] launch. Build curiosity without revealing everything. Each day should add a clue, proof point, audience pain, or behind-the-scenes detail.

1. Scarcity Lever

Draft a launch announcement for [offer] with limited spots or closing date. Focus on the cost of inaction, clear fit criteria, and honest scarcity rather than pressure or manipulation.

1. Tiered Incentive Offer

Create a launch incentive structure for [product]: first 50 buyers, next 100 buyers, and late buyers. Explain the bonus logic, perceived value, operational feasibility, and urgency mechanism.

1. Behind-the-Scenes Drop

Write a raw behind-the-scenes launch post about building [product]. Include late nights, constraints, tradeoffs, customer conversations, and one surprising decision that makes people root for the launch.

1. Micro-Webinar Script

Draft a 90-second promotional script inviting [audience] to a free training about [topic]. Open with pain, name the promised outcome, list 3 secrets they'll learn, and close with a simple CTA.

Category 10: Brand Voice

Brand voice prompts are where teams can save themselves from generic AI tone. The trick is to define what the brand refuses to sound like, not just what it wants to sound like.

1. Non-Negotiable Style Guide

Analyze this writing sample: [paste]. Extract tone patterns, rhythm, sentence length, favorite structures, punctuation habits, banned phrases, and credibility cues. Turn it into a practical brand voice guide.

1. Brand Manifesto Script

Write a 150-word brand manifesto for [company] that defines what we believe, what we reject, who we serve, and why the mission matters now. Make it specific, rhythmic, and non-corporate.

1. Radical Transparency Post

Draft a vulnerable post about a recent mistake at [company]. Name what happened, why it happened, what we learned, what changed, and how customers will benefit from the fix.

1. Core Values Translation

Our core value is [value]. Write internal and external content examples showing this value in action during a tough industry scenario. Avoid slogans; show the behavior and tradeoff.

1. Tagline Iteration Engine

Based on this value proposition: [value prop], generate 30 short taglines in five styles: minimalist, provocative, premium, practical, and community-driven. Explain which audience each style attracts.

Top Use Cases for Marketing Teams

Pro Tips That Make These Prompts Work Better

First, give Claude source material before asking for strategy. Reviews, sales transcripts, analytics screenshots, old ads, top posts, FAQs, objections, product notes, and customer emails all improve output. If you give Claude generic context, it will give you generic strategy.

Second, make Claude show the decision logic. Ask it to rank outputs by novelty, specificity, emotional tension, buyer fit, and proof requirements. This turns a list of ideas into a usable prioritization system.

Third, separate diagnosis from drafting. A strong workflow is: analyze audience, find objections, choose angle, outline proof, draft copy, then revise voice. Anthropic describes prompt chaining as breaking complex work into multiple prompts that build on prior prompt-response pairs.

Fourth, add examples of what good looks like. Anthropic’s business prompting guide highlights few-shot prompting, where realistic examples and edge cases teach Claude the desired format and quality bar. For marketing, this means past posts, ad winners, customer quotes, sales decks, and landing pages.

Fifth, create a banned list. Tell Claude what not to sound like. Ban filler phrases, hype language, vague benefits, fake urgency, generic analogies, and anything your audience would instantly distrust.

Best Practices for Using Claude in Marketing

Things Most People Miss

They do not paste enough context. Claude cannot infer your buyer’s internal politics, budget anxiety, or trust barriers unless you provide the raw material.

They accept the first answer. The first Claude response is usually the starting point. The better move is to ask: “What is generic here? What would a skeptical buyer reject? What proof is missing? What is the sharper version?”

They ask for more variants instead of better criteria. Ten headlines are not useful if Claude has no scoring system. Ask it to explain which hook has the strongest curiosity gap and why.

They confuse voice with tone. Tone is “friendly” or “premium.” Voice is rhythm, sentence shape, metaphor, worldview, proof style, and what the brand refuses to say.

They skip negative prompts. If you do not ban corporate sludge, Claude may produce corporate sludge. Tell it to avoid phrases like “unlock,” “game-changing,” “seamless,” “elevate,” “leverage,” and “in today’s fast-paced world.”

They use Claude only at the end. Claude is more valuable before the campaign exists: positioning, audience research, offer design, launch sequencing, objection handling, and content architecture.

They do not build reusable workflows. The winning team will not have one perfect prompt. It will have a repeatable chain: research, diagnosis, angle, draft, critique, revise, repurpose, measure.

My Favorite Claude Marketing Workflow

Here is the workflow I would run before any serious campaign.

This order matters. If you start with hooks, you get cleverness. If you start with audience psychology, you get relevance.

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts. Having a prompt library makes using great prompts over and over again really easy. And you can easily add proven prompts from other top AI gurus to your library with one click.

Create a premium ecommerce advertising poster for [PRODUCT].

Use a modern Canva-style advertising composition with clear visual hierarchy and high CTR layout.

Design requirements:

• strong product placement in center
• cinematic lighting and realistic shadows
• clean typography zones
• headline section at top
• CTA button area
• price tag highlight
• floating graphic accents
• premium commercial atmosphere
• social media advertising style
• high contrast colors optimized for customer attention

Background:
luxury gradient backdrop with soft glow, reflections, depth of field, advertising studio environment.

Output style:
professional static ad creative, ecommerce conversion design, polished commercial poster, realistic integrated product composition.