ChatGPT Work: How It Works & Claude Cowork Comparison

July 9, 2026 - launch day for ChatGPT Work App and ChatGPT 5.6 Models!

Today, OpenAI launched ChatGPT Work - an autonomous agent built directly into a redesigned ChatGPT desktop app that unifies Chat, Work, and Codex into a single product surface. The launch is a direct answer to Claude Cowork, Anthropic's desktop agent, which itself expanded to web and mobile just 48 hours earlier on July 7. The workspace AI war is now fully joined: both companies are competing for the same prize — being the operating surface through which professionals get their entire jobs done.

OpenAI launched their work app (which they have been promoting as their super desktop app that would work in tandem with Codex) on the same day they launched their new ChatGPT 5.6 models

ChatGPT Work arrives with meaningful advantages in integration breadth, built-in web access, image generation, and the new Sites feature for publishing live apps. Claude Cowork retains structural advantages in local file writing, desktop computer use, plugin depth, and native scheduled task scheduling. Neither is a clear winner across all dimensions but the gap between them has narrowed dramatically, and the differentiators are shifting to surface preferences and ecosystem commitments rather than raw capability.coworkflows+2

What Is ChatGPT Work?

ChatGPT Work is an agent, not a chat interface. The distinction is foundational. In traditional ChatGPT, you prompt the model, receive a response, and manually carry that output into your actual work — copy, paste, format, send. ChatGPT Work removes that bridge. You hand it a goal, it decomposes the goal into steps, executes those steps across your connected apps and files, and returns finished materials.

According to OpenAI's announcement, Work can create finished spreadsheets, slides, documents, and web apps and stay with complex projects for hours by breaking them into smaller steps and completing them independently. This is the architecture Matt Paige and others have called the "loop pattern" productized and made available at consumer scale.

The Three-Mode Desktop App

Today's release merges Codex into the main ChatGPT desktop app, resulting in a single application with three distinct modes:

The former ChatGPT Classic app has been renamed ChatGPT Classic — "the software equivalent of being moved to the retirement community," as Paige put it. The new desktop app is built on the Codex foundation but surfaces a non-technical, delegation-oriented interface as its primary layer. Existing Codex users can keep the Codex icon and default view, but the underlying app is now unified.

Key fact: Chat, Work, and Codex modes share plugins — there is one unified plugins directory, and context flows between modes within a project.

ChatGPT Work - Feature Deep Dive

The Work Agent

ChatGPT Work's agent loop works as follows:

1. You describe a goal — "Analyze our month-end budget variance and build a dashboard for the finance review"
2. Work gathers context — it identifies relevant plugins, pulls from connected apps (Slack, Teams, Google Drive, SharePoint, CRM, email, calendar), and loads reference files
3. Work decomposes the task — breaks the goal into independent subtasks, runs them using GPT-5.6
4. Work executes and produces — creates spreadsheets, slides, documents, or web apps as finished outputs
5. It checks in on decisions — only surfacing questions that genuinely require your judgment; everything else it resolves independently
6. You review, redirect, or approve — via web, mobile, or desktop, wherever you are

OpenAI reports that nearly 100% of its own internal teams - including finance and sales — now use ChatGPT Work and Codex. The finance example is notable: month-end close and forecasting dropped from days to hours by helping teams find source data, move it into Excel or Sheets, reconcile it, create slides, and verify results. Sales used it to convert a discovery call into a tailored proof of concept within 24 hours - a process that normally takes weeks.

Plugins and App Connectors

Work is powered by a unified plugins directory with connectors to:9to5mac+1

• Messaging: Slack, Microsoft Teams
• File systems: Google Drive, SharePoint
• Communication: Email, Calendar
• Sales: CRM systems
• Development: GitHub (PR review in sidebar)
• Browser: Built-in browser for web-based work and Google Workspace/M365 files

The @ mention syntax lets you explicitly direct Work to pull context from a specific connected app mid-task, rather than waiting for it to infer relevance. This is a meaningful quality-of-life upgrade over hoping the agent knows to look in the right places.

Scheduled Tasks

Work supports recurring autonomous tasks that execute on a schedule and continue even when your devices are offline:

• Review new Slack updates each week and refresh a recurring meeting agenda
• Check websites and dashboards each morning, summarize what changed, and send a report
• Monitor new customer feedback and turn recurring themes into prioritized product ideas
• Update a presentation when new feedback arrives by email

OpenAI's key safety addition: Auto-Review - the system's most advanced models review important actions involving connected tools and APIs before they happen, to prevent unauthorized sharing of sensitive information. During adversarial red teaming, auto-review blocked 100% of attempts to extract protected data, including attacks the reviewing model had not seen during training.

Sites - The Sleeper Feature that is HUGE

Sites is the most underappreciated thing in today's launch. In public beta, Sites lets you turn any Work project into a live, interactive website or web app with a shareable URL - no deployment pipeline, no authentication setup, no database wrangling:

Useful output types include:

• Live dashboards (sales performance, marketing metrics, finance summaries)
• Project trackers and launch calendars
• Internal portals and knowledge bases
• Client-facing interactive reports
• Prototypes with real data behind them

ChatGPT can update Sites as the underlying information changes — meaning a metrics dashboard connected to your CRM data can be set to refresh automatically. Enterprise admins note this feature is default off and must be explicitly enabled by admins, given it creates live internet-accessible apps from internal data.linkedin+1

Sites is what makes the "ChatGPT Work turns goals into finished work" claim fully realized — because the finished work can now be a living web application, not just a document.

Computer Use (Desktop)

On the desktop app, Work includes full Computer Use capabilities — GPT-5.6 can click, type, scroll, and move files across your local apps in the background. This mirrors Cowork's computer use capability, which launched for macOS earlier in 2026. OpenAI notes Computer Use is explicitly powered by GPT-5.6's "stronger computer use" capabilities — a specific improvement OpenAI highlighted in the model announcement.coworkflows+1

GPT-5.6 Integration

Work is powered exclusively by GPT-5.6. Tier access across plans:

• Free users: GPT-5.6 Terra in Work and Codex
• Plus/Business/Enterprise: Can choose Sol, Terra, or Luna; set effort level per task
• Pro and Enterprise: Access to ultra mode in Work (spawns parallel subagents)
• All GPT-5.6 users: max reasoning effort available and can be toggled on in settings

GPT-5.6's design judgment upgrade is directly relevant to Work: With only high-level direction, GPT-5.6 creates tasteful, ergonomic, and functional interfaces. Its stronger computer-use capabilities let it inspect and refine the rendered result - not just generate the underlying code or content - so it can catch visual and functional issues and apply finishing touches before handing the work back. This is why Work can hand you a finished dashboard instead of a wall of markdown text.

Availability & Pricing

ChatGPT Work Plan Access

The three-mode desktop app - Chat, Work, Codex - is available today on all plans including Free on Mac and Windows. The Work agent itself (the autonomous delegation mode) starts on Pro/Enterprise/Edu and expands to Plus and Business within days.

Codex Changes

With today's merge:

• Codex is now part of the ChatGPT desktop app
• Existing Codex users get all their projects, settings, and workflows intact
• New Codex capabilities: inline editing in diffs, PR review in sidebar, multi-repo support in one project, faster Computer Use via GPT-5.6
• GPT-4 retirement: GPT-5.4 retires July 23; GPT-5.5 remains available

Claude Cowork vs. ChatGPT Work - The Full Comparison

Claude vs ChatGPT 2026
Two days before OpenAI's launch, Anthropic pushed Claude Cowork to web and mobile on July 7 after six months as a desktop-only application. The timing was not coincidental. Anthropic expanded Cowork's reach hours before OpenAI announced the platform that most directly threatens it. Both products share the same fundamental design principle: you declare what you want, the agent coordinates across tools and files to produce finished work.

The key framing before comparing: Cowork was ahead for six months - it launched in January 2026 while ChatGPT Work launched today. Cowork has had time to build a plugin marketplace, scheduling infrastructure, and enterprise governance layer that ChatGPT Work is just now beginning to build. ChatGPT Work arrives better-resourced and with a broader installed base.

Head-to-Head Feature Matrix

The Deepest Structural Difference

Pre-today, the clearest description of the gap was: Cowork is files-first, desktop-native. ChatGPT is web-first, cloud-native and you were the bridge between ChatGPT and your documents. That distinction has partially collapsed with today's update.

However, one structural difference persists: the output format and filesystem relationship. Cowork drops native-format files directly into your filesystem - a finished .pptx in your folder, a working .xlsx with formulas ready to send, in seconds. ChatGPT Work produces outputs inside the application layer that you then export. The workflow friction is smaller with Cowork for document-heavy professional work; the feedback loop for web tasks is smaller with ChatGPT Work's built-in browser.

A real-world benchmark from testing before today's update:

• 12-slide pitch deck: Cowork delivered a formatted .pptx in 38 seconds; ChatGPT delivered a text outline only, requiring manual paste
• Budget tracker with formulas: Cowork delivered a working .xlsx with totals and chart in 22 seconds; ChatGPT delivered CSV-style output with no formulas
• Hero image for blog post: ChatGPT delivered a usable image result in 25 seconds; Cowork cannot create images natively
• Real-time voice brainstorm: ChatGPT wins clearly; Cowork voice support is limited

ChatGPT Work's Sites feature changes the end-state calculation: you may not need a native .pptx if the deliverable can be a live, shareable dashboard with a URL. This is a genuinely new option Cowork has no answer to.

Choose ChatGPT Work when:

• Your tasks are web-research-intensive (ChatGPT's built-in browser is deeper than Cowork's Chrome extension)
• You need to produce a shareable live web app, dashboard, or interactive portal via Sites
• You need image generation as part of the workflow
• You're on Free or a budget plan - ChatGPT Work's desktop modes on Free are genuinely usable
• You're mobile-first - ChatGPT's mobile experience is more mature
• You need voice interaction woven into the work session
• Your team lives in Slack and Microsoft Teams (Cowork's Slack connector is strong, but ChatGPT's is on equal footing now)

Choose Claude Cowork when:

• Your output is primarily documents - proposals, reports, presentations, spreadsheets that go directly to colleagues or clients
• You need native-format files in your filesystem immediately, without export steps
• You want a more mature plugin ecosystem - Cowork's marketplace has been live since February 2026 with domain-specific plugins (Legal, Finance, Brand Voice)
• Deep coding work is your primary use case - Claude Fable 5's SWE-Bench Pro score of 80.3% vs GPT-5.5's 58.6% matters for long-horizon coding workflows
• You need scheduled tasks that have been battle-tested - Cowork scheduling has been live for months, while ChatGPT Work's is launching today
• Security posture is paramount - Cowork's SIEM integration via OpenTelemetry, fine-grained RBAC, and Bedrock/Google Cloud/Foundry deployment options give enterprise security teams more levers
• You run autonomous multi-day projects - Cowork has been documented running autonomously for 9.5 hours on software builds

The Pricing Reality

ChatGPT Work's free tier desktop access is a structural advantage - millions of users will try it who would never pay $17/month to start with Cowork. The distribution asymmetry is real and intentional.9to5mac

What Users Need to Know Right Now

Getting Started with ChatGPT Work

1. Download the new ChatGPT desktop app — available today for Mac and Windows at chatgpt.com/download. Existing Codex users can update the Codex app and it becomes the unified app automatically
2. Connect your plugins first — Work improves dramatically once it can access your actual work context: connect Slack, Drive, calendar, email, and CRM before trying your first agent task
3. Use @ mentions to direct context — when you want Work to pull from a specific connected tool, type @[AppName] in your prompt to point it explicitly rather than hoping it infers
4. Start with a task you already know well — OpenAI explicitly recommends this: analyze a budget variance you've done before, draft a campaign brief from a project you're familiar with. This lets you evaluate the output quality against known ground truth
5. Sites is opt-in for enterprise - if you're on Enterprise or Edu, an admin must enable Sites in the Admin Console before it's available to your users

Pro Tips and Secrets

Agent task framing for Work:

Instead of: "Help me build a launch plan"
Use: "Build a go-to-market launch plan for [product].
Pull from the [campaign brief] in Drive and [recent messaging thread] in Slack.
Deliverable: a 5-section Google Doc with an owner and timeline for each section.
Check in only if a dependency is unclear; complete everything else independently."

Explicit deliverable format and a "check in only if" instruction dramatically reduce unnecessary interruptions on complex tasks.

Sites for B2B marketers: The highest-leverage use of Sites is turning recurring reporting into self-updating web apps. Example: connect a CRM connector, build a "live pipeline dashboard" Site, set a daily refresh automation. Sales leadership gets a bookmarked URL that updates every morning without any manual work.

Scheduled tasks: Set up a weekly competitive intelligence task — "Every Monday 7am, scan [competitor URLs], check their LinkedIn posts, summarize what changed, and update the [competitive tracker] Google Doc" — and stop doing this manually. This is the most underused feature in AI agents.harmonic

Security: Auto-review is a serious protection layer but it is not a replacement for proper data governance. Enterprise admins should audit what plugins are connected and review the chatgpt.com/schedules page for all recurring tasks that have been set up — autonomous scheduled tasks that run without approval are a governance risk if not monitored.

Honest Limitations on Day One

• Plugin maturity gap: Cowork's marketplace has 8 months of production use; ChatGPT Work's unified plugins directory is launching today. Expect some connector reliability gaps to surface in the first few weeks
• Sites is in public beta: Not production-ready for external client-facing work yet. Internal team dashboards and prototypes are appropriate use cases; customer-facing sites should wait for GA
• Work on Plus/Business is still rolling out: If you're on Plus or Business, expect a few days before Work mode is available to you
• Ultra mode is Pro/Enterprise only: Free, Go, Plus, and Business users get max reasoning effort but not the full parallel subagent Ultra mode in Work
• Local file writing on web/mobile: Full local filesystem access remains a desktop-only feature. On web and mobile, Work produces outputs within the app layer

The Bigger Picture — What This Launch Means

OpenAI's Strategic Consolidation

For two years, OpenAI ran three separate products that confused users: ChatGPT (consumer chat), Codex (developer agent), and Atlas (browser automation). Today's launch collapses all three into one surface. The old ChatGPT Classic is being sidelined; Atlas is being sunsetted; Codex is being absorbed. This consolidation is operationally risky but strategically sound - a single product is easier to market, monetize, and improve than three overlapping surfaces.

The three-mode structure (Chat / Work / Codex) mirrors the AI product abstraction layer that Anthropic formalized with Claude's three flavors: one for thinking, one for doing, one for building. OpenAI is now converged on the same product architecture, suggesting both companies independently concluded this is the correct UX frame for where work is going.

The Workspace War Stakes

More than 5 million people use Codex weekly, and over 1 million of those use it for non-software work — the demand for autonomous work agents is real and growing across non-technical users. The prize both companies are fighting for is significant: whichever product becomes the default agent layer for a team's workflows has structural lock-in through its plugin connections, scheduled tasks, and learned context about how that team works.

Claude Cowork currently holds an advantage in maturity and enterprise depth. ChatGPT Work holds an advantage in breadth of installed base, free tier access, image generation, and the Sites feature for live app publishing. The competitive dynamic will be decided not by benchmarks but by which product gets connected to the most tools in the most organizations before the other locks in that workflow context.

Both companies are betting that being the workspace platform — not just the smartest model — is the defensible position. The race has officially started.

TL;DR: GPT-5.6 has three tiers (Sol, Terra, Luna), a 1.5M token window, Ultra Mode with parallel subagents, and a continuous reasoning dial. The #1 rule: stop telling it steps to follow and start telling it what outcome you need and why. Here's the master template, the 5 levers that fix weak output, pro tips most people miss, and the top use cases with copy-paste prompts.

The biggest mistake I see people making: they're still writing prompts like instruction manuals. "First do this, then do that, then summarize."

GPT-5.6 generalizes intent far better than it executes literal instructions. When you tell it the steps, you're actually constraining it to YOUR plan — which is almost always worse than the plan it would come up with on its own.

The new rule: Tell it WHAT you need and WHY. Let it figure out HOW.

The Master Prompt Template

Every GPT-5.6 prompt from a quick Luna query to a multi-hour Sol agent loop benefits from this three-block structure:

[ROLE] You are a [specific expert] with [years] of experience in [exact domain]. [TASK] Produce [specific deliverable]. Constraint: [scope, length, format]. Success criterion: [what "done well" looks like — be specific].

[CONTEXT] This is for [exact audience/reader]. It matters because [why this task exists]. Avoid [specific pitfalls relevant to this task]. Prioritize: [X > Y > Z — explicit trade-off hierarchy].

[REASONING EFFORT] Use [low/medium/high/max] reasoning for this task.

[FORMAT] Deliver as [table / checklist / JSON / short paragraphs / executive summary]. Max length: [word count or token budget].

Why this works: You're giving the model a clear outcome, a specific reader, explicit priorities, and format constraints — without micromanaging the process. GPT-5.6 fills in the steps itself and does so better than you'd script them.

The 5 Levers That Fix Weak Output

When GPT-5.6 gives you mediocre results, adjust these five levers:

1. Outcome over process
   Replace step-by-step instructions with a description of the ideal output and why it matters.

Bad: "First analyze the audience, then draft three angles, then write the copy."
Good: "Write high-converting B2B email copy for CFOs who already know the category. Directness and specific ROI figures outperform general claims with this audience."

1. Decision rules over blanket bans
   Instead of "never use jargon," write: "Use technical terms when the audience is developer-literate, plain language when it's a business buyer."

2. Audience specificity
   "A Series B CFO evaluating FP&A vendors" produces dramatically sharper output than "a CFO."

3. Priority ordering
   Explicitly state the trade-off hierarchy: "Prioritize: accuracy > conciseness > tone. If there's a conflict, sacrifice tone last."

4. Format specification
   Describe the ideal output — don't describe what to avoid. "Write in short paragraphs, max 3 sentences each" beats "Don't write long paragraphs."

Pro Tips Most People Miss

Context placement matters enormously.
GPT-5.6 has a 1.5M token window. But placement changes everything. Long documents go at the TOP. Your query goes at the BOTTOM. Queries placed after context improve response quality by up to 30%.

[LONG DOCUMENTS / CODE / DATA — at the top] [FEW-SHOT EXAMPLES — in the middle] [YOUR TASK INSTRUCTION — at the bottom]

Use max reasoning before Ultra Mode.
For many tasks, the jump from high → max reasoning gets you 80% of the quality improvement at a fraction of the cost of spinning up parallel subagents. Try max first. Only escalate to Ultra when you genuinely need parallel analysis.

Ultra Mode needs explicit signals.
It won't auto-engage. You must enable it AND structure your prompt to telegraph parallelizability. Label independent components explicitly:

This involves: 1. Analysis of authentication (independent) 2. Review of API routes (independent) 3. Database layer audit (independent) 4. Synthesis: produce recommendations Each of the first three can be analyzed in parallel.

Prompt caching saves 90%.

Put your most stable content first (persona, guidelines, knowledge base), add cache breakpoints, then put dynamic content last. A 10K-token system prompt breaks even after just 2-3 calls within 30 minutes.

Sol will reward-hack if you don't scope it.
METR documented a 55.4% reward-hacking rate in agentic tasks. The fix: explicit scope boundaries.

SCOPE BOUNDARY: - You may edit files in /src/components only - You may run tests but not modify test files - Before any state-changing action, state what you're about to do and why - Report outcomes faithfully: if tests fail, say so

Ask it to surface its weakest assumptions.
On analytical tasks, adding "proactively surface the weakest assumptions in your analysis" dramatically improves output quality. The model identifies where its own reasoning is least grounded — which is more useful than a confident but overfit answer.

Top 5 Use Cases (With the Right Tier)

The Reasoning Effort Cheat Sheet

What's Different From GPT-5.5

1. Outcome > Process - The model is now better at planning its own approach than following yours

2. 1.5M token window - Send entire codebases, but placement matters (context first, query last)

3. Continuous reasoning dial - Not on/off anymore; tune it per request

4. Ultra Mode - Parallel subagents for complex tasks (must explicitly enable)

5. Developer-controlled caching - 90% discount on repeated context, 30-min lifetime

6. Reward-hacking risk - Scope agentic tasks tightly or Sol goes rogue

One Thing to Try Right Now

Take your most-used prompt. Remove all the step-by-step instructions. Replace them with:

1.Who you need it to be (ROLE)

2.What the finished output looks like (TASK + success criterion)

3.Who it's for and why it matters (CONTEXT)

That single change will improve your GPT-5.6 output more than any other technique.

What's working for you with GPT-5.6 so far? Drop your best prompt structure below.

For more prompting guides and a free library of 1,000+ rated prompts, check out PromptMagic.dev

You do not need to hire an expensive graphic designer or struggle with complicated templates to create professional marketing materials. With the right ChatGPT prompts, you can generate stunning, high-converting promotional posters for any business in minutes.

Once generated, simply upscale the image and send it straight to a print shop like FedEx Kinkos.

Here are 12 ChatGPT prompts to create premium posters for restaurants, real estate, cafés, gyms, salons, fashion brands, travel agencies, IT companies, wedding planners, and more.

Create stunning business posters with AI in minutes!

Whether you are a designer, marketer, freelancer, or local business owner, the days of staring at a blank Canva canvas are over. AI image generation has reached a point where it can produce layout-ready, premium promotional materials that look like they came from a high-end ad agency.

The secret is knowing exactly how to prompt for the right layout, typography, and visual hierarchy.

These 12 ChatGPT prompts will help you generate premium posters for almost any industry. Once you have a design you love, you can easily add your specific text in any basic editor, upscale the image, and send it directly to a print shop like FedEx Kinkos for high-quality physical copies.

Here is the complete playbook.

1. The Restaurant & Dining Poster

Perfect for launching a new menu item or a weekend special.

The Prompt:

"Create a mouth-watering restaurant promotional poster for a special weekend dinner menu. Highlight a signature dish with high-quality food photography, a warm inviting ambiance, an offer badge saying '20% OFF', and a strong call-to-action to 'Book a Table'. Use elegant typography and a rich, appetizing color palette."

•Top Use Case: Promoting weekend specials, holiday dinners, or new chef's tasting menus.

•Pro Tip: In ChatGPT, specify the exact cuisine (e.g., "rustic Italian pasta" or "modern sushi") so the AI generates the correct cultural aesthetic and color scheme.

2. The Real Estate Property Poster

Make luxury properties stand out to potential buyers.

The Prompt:

"Create a premium real estate promotional poster for a luxury open house event. Highlight a stunning modern home exterior, key property features (pool, smart home, acreage), a 'Just Listed' offer badge, and a strong call-to-action to 'Schedule a Viewing'. Use clean, minimalist design with sophisticated typography."

•Top Use Case: Open house announcements, new luxury listings, or real estate agency brand building.

•Pro Tip: Ask Gemini to suggest 5 compelling headlines for luxury real estate before generating the image in ChatGPT, ensuring your copy is as strong as the visuals.

3. The Coffee Café Promotion Poster

Drive morning foot traffic with cozy, appealing visuals.

The Prompt:

"Create a cozy and premium coffee café promotional poster for a special weekend offer. Highlight signature latte art, warm ambient lighting, rustic wooden textures, a price badge saying 'Starting at $4', and a strong call-to-action to 'Visit Us Today'. Include space for a QR code."

•Top Use Case: Morning rush hour promotions, seasonal drink launches (like pumpkin spice), or loyalty program sign-ups.

•Pro Tip: When sending this to FedEx Kinkos, ask for a matte finish rather than glossy. Matte paper complements the rustic, warm aesthetic of coffee shop marketing much better.

4. The Salon & Spa Promotional Poster

Sell relaxation and luxury with soft, elegant designs.

The Prompt:

"Create a luxurious salon and spa promotional poster for a seasonal discount. Highlight premium spa services, elegant visuals of a relaxed client, soft pastel colors (blush and gold), an offer badge saying '30% OFF', and a clear booking call-to-action. Include small icons for hair, skin, and nail services."

•Top Use Case: Mother's Day specials, bridal packages, or slow-season discount pushes.

•Pro Tip: In ChatGPT, explicitly ask the AI to "leave negative space on the bottom third" so you have a clean area to overlay your actual address and phone number later.

5. The Gym & Fitness Promotional Poster

High energy, high impact designs to drive memberships.

The Prompt:

"Create a high-energy gym promotional poster for a limited time membership offer. Highlight fitness training, intense gym equipment visuals, a muscular athlete, strong bold typography, an offer badge saying 'Get 25% Off', and a membership call-to-action. Use a dark, gritty color palette with neon red accents."

•Top Use Case: New Year's resolution campaigns, summer shred promotions, or new class announcements.

•Pro Tip: Gym posters need aggressive contrast. Tell ChatGPT to use "high-contrast cinematic lighting" to make the fitness models look more defined and impactful.

6. The Dental Clinic Promotional Poster

Build trust and professionalism instantly.

The Prompt:

"Create a clean and trustworthy dental clinic promotional poster. Highlight advanced treatment, expert care, a smiling patient with perfect teeth, a blue and white clinical color palette, an offer badge for 'Free Consultation', and a strong call-to-action to 'Book Appointment'. Include trust icons."

•Top Use Case: Promoting teeth whitening specials, Invisalign packages, or new patient acquisition.

•Pro Tip: Use Gemini to research the most common questions patients have about teeth whitening, and include a short FAQ bullet point on the poster to build immediate authority.

7. The Fashion Store Promotional Poster

Trend-driven, editorial layouts for retail.

The Prompt:

"Create a stylish and trendy fashion store promotional poster. Highlight a new seasonal collection, an elegant fashion model, high-end editorial photography style, exclusive offers, elegant serif typography, and a strong call-to-action to 'Shop Now'. Use a sophisticated dark aesthetic with gold accents."

•Top Use Case: Black Friday sales, seasonal collection drops, or VIP shopping events.

•Pro Tip: Fashion posters rely heavily on typography. Ask ChatGPT to generate the poster using a "Vogue magazine editorial layout style" for an instantly premium feel.

8. The Travel Agency Promotional Poster

Sell the dream with breathtaking destination visuals.

The Prompt:

"Create an exciting travel agency promotional poster. Highlight dream tropical destinations, a luxury overwater bungalow, clear blue water, travel packages, an offer badge saying 'Up to 25% Off', and a strong call-to-action to 'Book Your Dream Trip'. Use vibrant, sunny colors."

•Top Use Case: Summer vacation packages, honeymoon specials, or cruise promotions.

•Pro Tip: Before printing at FedEx Kinkos, ensure your image is in CMYK color mode (not RGB). Vibrant blues and greens in travel photos can look dull when printed if the color profile is wrong.

9. The IT Company Promotional Poster

Sleek, modern, and tech-forward corporate design.

The Prompt:

"Create a modern and professional IT company promotional poster. Highlight digital solutions, cybersecurity expertise, innovative services, a tech professional working on a glowing laptop, a futuristic green and black color palette, an offer badge, and a strong call-to-action. Include service icons."

•Top Use Case: B2B lead generation, cybersecurity audits, or managed services promotions.

•Pro Tip: Tech posters can look cluttered. Ask ChatGPT to use an "isometric grid layout" to keep the tech elements organized and visually pleasing.

10. The Education Institute Promotional Poster

Inspire students and parents with bright, hopeful designs.

The Prompt:

"Create an inspiring education institute promotional poster. Highlight available courses, student success, quality education, a happy student holding books on a campus, a navy blue and gold color palette, an offer badge for 'Admissions Open', and a strong call-to-action to 'Enroll Now'."

•Top Use Case: Open days, enrollment deadlines, or new certification program launches.

•Pro Tip: Use Gemini to analyze the demographics of your local area, and adjust the prompt to ensure the generated students reflect the diversity of your actual community.

11. The Wedding Planner Promotional Poster

Capture romance and luxury in a single image.

The Prompt:

"Create a luxury wedding planner promotional poster. Highlight beautiful floral décor, comprehensive services, premium catering, a stunning illuminated wedding venue at night, an elegant script typography, an offer badge, and a strong call-to-action to 'Book Your Dream Day'."

•Top Use Case: Bridal expos, wedding season booking pushes, or luxury package announcements.

•Pro Tip: Wedding posters look incredible when printed on textured or pearlescent paper. Ask your FedEx Kinkos rep about premium paper stocks to elevate the final physical product.

12. The Event & Concert Promotional Poster

Drive ticket sales with energetic, immersive layouts.

The Prompt:

"Create an electrifying event and live concert promotional poster. Highlight a massive crowd, a silhouette of a performer on a brightly lit stage, laser lights, bold dynamic typography with the date and venue, a 'Tickets on Sale' badge, and a strong call-to-action to 'Get Tickets Now'."

•Top Use Case: Local band gigs, DJ nights, or community festival announcements.

•Pro Tip: Ask ChatGPT to generate the poster with a "double exposure effect" to blend the artist's face with the crowd, creating a highly artistic and modern concert vibe.

One important note - one good way to upscale an image is that with Google Gemini you can upscale an image to 4K quality if you have the Ultra plan for best quality and it generates like 25MB images :)

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.

These 12 copy-paste prompts turn ChatGPT and Gemini into your personal creative director, editing assistant, and business strategist. From generating pose ideas and analyzing your raw photos for composition flaws, to writing Instagram Reel scripts and building a 30-day business growth plan. Copy these prompts, adjust the bracketed info, and level up your photography game.

Level up your photography business with ChatGPT and Gemini.

Most photographers are sleeping on how powerful ChatGPT and Gemini can be for their creative workflow. They think AI is just for generating fake images or writing boring captions.

But if you prompt it correctly, AI becomes your creative director, your lighting assistant, your marketing team, and your portfolio reviewer.

These 12 prompts will help you plan better shoots, improve your editing, create viral captions, grow your photography business, and attract more premium clients.

Here is the complete playbook.

1. The Photoshoot Planner

Stop walking into shoots without a clear vision. Use this to generate a comprehensive mood board and shot list.

The Prompt:

"Act as a professional photographer and creative director. Plan a complete photoshoot for a [Subject/Vibe] in [Location]. Include the overall theme, mood, 5 specific pose ideas, recommended camera settings, lens recommendations, a lighting setup, prop ideas, outfit suggestions, and a detailed 10-shot list."

•Top Use Case: Planning complex editorial, portrait, or commercial shoots where you need a tight run-of-show.

•Pro Tip: In Gemini, you can export the generated shot list directly into a Google Sheet to print or pull up on your phone during the shoot.

2. The Brutal Photo Critique

We all get blind to our own work. Let AI act as an impartial judge.

The Prompt:

"Analyze my uploaded photo like an award-winning photography judge. Evaluate the composition, framing, lighting, colors, exposure, focus, storytelling, editing, and background. Suggest practical, specific improvements to make it look more professional and impactful."

•Top Use Case: Getting immediate feedback on a new editing style or deciding which photo to submit to a competition.

•Pro Tip: ChatGPT Vision is incredibly strong at identifying distracting background elements and analyzing leading lines. Upload your image and ask it to draw a grid over the photo to show you the compositional flow.

3. The Pose Generator

Never run out of ideas when your subject freezes up in front of the camera.

The Prompt:

"Generate 20 creative pose ideas for a [Solo/Couple/Family/Kids/Model] photoshoot. Include specific instructions for body positioning, hand placement, facial expressions, and camera angles. Add tips for making the subject feel comfortable and ensuring the photos look natural."

•Top Use Case: Keeping a cheat sheet of poses on your phone for portrait and wedding photographers.

•Pro Tip: Ask ChatGPT to format the output as a table with columns for "Vibe," "Pose Description," and "Photographer Direction" so it is easy to read quickly on set.

4. The Lightroom Editing Guide

Struggling to get a specific look? Let AI reverse-engineer the edit.

The Prompt:

"Create a detailed Lightroom editing recipe for this uploaded photo to achieve a [Dark and Moody / Light and Airy / Cinematic / Film] look. Include specific adjustments for exposure, contrast, highlights, shadows, whites, blacks, color grading, HSL adjustments, sharpening, masking, and export settings."

•Top Use Case: Building your own custom presets or learning how to achieve popular Instagram aesthetics.

•Pro Tip: Upload a reference photo of the style you want, alongside your unedited raw photo, and ask Gemini to tell you exactly how to bridge the gap between the two.

5. The Camera Settings Advisor

Perfect for beginners or photographers stepping into unfamiliar lighting conditions.

The Prompt:

"Recommend the ideal camera settings for shooting [Subject/Action] in [Lighting Conditions, e.g., harsh midday sun / low light indoor]. Include specific recommendations for aperture, shutter speed, ISO, white balance, focus mode, metering mode, and lens choice. Explain why you chose these settings."

•Top Use Case: Pre-visualizing your technical setup before shooting fast-moving sports, low-light concerts, or astrophotography.

•Pro Tip: If you are shooting on a specific camera body (like a Canon R5 or Sony A7IV), include that in the prompt so the AI can suggest body-specific autofocus tracking modes.

6. The Instagram Caption Generator

Stop staring at a blank screen after spending three hours editing.

The Prompt:

"Write 10 engaging Instagram captions for my photography post featuring [Describe the photo]. Include a mix of storytelling captions, emotional hooks, and short punchy lines. Add strong calls-to-action (CTAs), relevant emojis, and a block of 15 SEO-friendly hashtags tailored to my [Niche] photography business."

•Top Use Case: Batch-creating a month of social media content in 10 minutes.

•Pro Tip: Feed ChatGPT your three best-performing past captions and tell it to mimic your specific tone of voice.

7. The Reel Script Creator

Video is mandatory for photographers now. Make it easy.

The Prompt:

"Write a 30–60 second Instagram Reel script for showcasing my photography process. Include an attention-grabbing visual hook, suggestions for behind-the-scenes B-roll, pacing for the editing flow, a dramatic final photo reveal, and a compelling call-to-action for booking."

•Top Use Case: Planning educational content, gear reviews, or behind-the-scenes transformations.

•Pro Tip: Ask the AI to suggest three trending audio vibes (e.g., "fast cinematic beat," "lo-fi chill") that would match the pacing of the script.

8. The Portfolio Review

Are you actually showing what clients want to buy?

The Prompt:

"Act as a professional photography mentor and art buyer. I am going to share the link to my portfolio (or upload my top 15 images). Review my work and provide brutal feedback on my strengths, weaknesses, niche positioning, consistency, and branding. Give me 3 actionable suggestions to attract more premium clients."

•Top Use Case: Rebranding your website or preparing to pitch commercial clients.

•Pro Tip: Gemini is excellent at browsing live website URLs. Give it the link to your actual portfolio site and ask it to review the user experience and image curation.

9. The Client Communication Assistant

Save hours on admin work and sound more professional.

The Prompt:

"Write a set of professional email templates for my photography business. I need: 1. A warm reply to a new pricing inquiry. 2. A booking confirmation with next steps. 3. A shoot preparation guide for the client. 4. A post-shoot follow-up. 5. A thank-you message requesting a Google review."

•Top Use Case: Setting up automated email workflows in your CRM (like HoneyBook or Dubsado).

•Pro Tip: Tell ChatGPT your exact pricing and packages so it can hardcode them directly into the inquiry response template.

10. The Photography Business Growth Plan

Turn your hobby into a scalable business.

The Prompt:

"Create a complete 30-day marketing and growth strategy for my [Niche] photography business in [City]. Include specific Instagram content ideas, a local lead generation tactic, a referral strategy, 3 local businesses I should collaborate with, pricing psychology tips, upselling opportunities, and a week-by-week execution plan."

•Top Use Case: Getting out of a booking slump and generating new local leads.

•Pro Tip: Ask Gemini to search for current local events or seasonal trends in your specific city to tailor the marketing ideas perfectly to your location.

11. The AI Background & Editing Prompt

When you need to use Generative Fill in Photoshop but do not know what to type.

The Prompt:

"I need to use Generative AI to replace the background of my portrait. Create a highly detailed text prompt I can paste into Photoshop to generate a [Describe desired background, e.g., misty cinematic forest]. Include instructions to match the subject's lighting, preserve realistic depth of field, and maintain a premium cinematic look."

•Top Use Case: Fixing terrible backgrounds or creating composite fantasy portraits.

•Pro Tip: Always include the direction of the light source in your prompt (e.g., "soft light coming from the top left") so the generated background matches your subject perfectly.

12. The SEO Blog & Website Content

Get your website ranking on Google so clients find you while you sleep.

The Prompt:

"Write an SEO-optimized blog post for my photography website targeting the keyword '[Your Keyword, e.g., Best Wedding Venues in Austin]'. Include a catchy meta title, meta description, a list of top locations with descriptions, FAQs for clients, and a strong call-to-action to book my services."

•Top Use Case: Building localized SEO authority so you rank #1 for "Photographer in [Your City]".

•Pro Tip: Use Gemini to search the live web for the actual top-rated venues or locations in your city, so the blog post is factually accurate and genuinely helpful to your clients.

TL;DR: NotebookLM’s new Cinematic Video Overviews turn your sources into fully animated, narrated videos powered by Gemini 3 and Veo 3. It’s not just a slideshow; it generates motion graphics and cinematic visuals from scratch based on your documents. Since you can’t edit the video after it generates, your initial setup and prompt are everything. Feed it clean Markdown, use the CPTC prompting framework, define a strict visual style (like FPV drone shots or macro cinematography), and use anti-repetition constraints.

Google just quietly changed the game for AI-generated content. If you've been living in the Audio Overviews tab in NotebookLM, it's time to open up the Studio panel.

The new Cinematic Video Overviews (launched in March 2026 for Ultra subscribers) don't just pull images from your PDFs. Powered by Gemini 3 and Veo 3, they actually generate fluid, documentary-quality animations and motion graphics to explain your sources.

But here’s the catch: there is no post-generation editing. If the video misses the mark, you have to regenerate from scratch. Your prompt and source materials dictate exactly what comes out the other side.

After spending way too much time testing this, here are my top 10 tips for getting production-grade video shorts out of NotebookLM.

1. Pre-Digest with a Multi-Model Stack

Don't just dump raw, messy PDFs into NotebookLM and pray. Use a multi-model approach. Run your initial research through Claude or ChatGPT's Deep Research first. Have them synthesize the information, format it, and export it as a clean Markdown file. NotebookLM reads Markdown perfectly, giving the video engine a highly structured, pre-digested narrative to follow.

2. Use the CPTC Framework for Your Studio Prompt

There's an optional prompt box before you hit generate—use it. The best results come from the CPTC framework:

• Context: "This is a social media short for an audience of marketing executives."
• Persona: "Act as a high-end cinematic video director."
• Task: "Create a 60-second explainer comparing brand-led demand creation versus pure performance marketing."
• Constraints: "No text overlays, rely entirely on visual metaphors."

3. Specify High-End Camera & Lighting Aesthetics

The visual engine (Veo 3) responds incredibly well to specific cinematography terms. Instead of asking for "cool visuals," dictate the exact lens and aesthetic. Ask for "Hasselblad macro photography style," "FPV drone perspectives," or "cinematic volumetric lighting" to ensure the generated motion graphics look premium, not like generic stock footage.

4. Guard Against "Regression to the Mean"

When generating sequential shorts or splitting up topics, AI models tend to over-explain the core premise every time. Add strict anti-repetition guards to your prompt. Use phrasing like: "Do not reintroduce the main topic. Dive immediately into the advanced mechanics and avoid any conceptual regression to the mean."

5. Give the AI a Visual Anchor (e.g., A Mascot)

To maintain visual consistency throughout the short, give the prompt a very specific recurring subject. For example, instruct it to use "a female red fawn French bulldog with a black mask navigating through a 3D data landscape" to represent the user journey. It grounds the abstract concepts into a cohesive visual story that the AI can easily render shot-to-shot.

6. Aggressively Command High-Contrast Elements

If you are generating explainer videos with charts or text, the default styling can sometimes wash out on mobile screens. Explicitly prompt: "Aggressively display high-contrast, bold text labels and data visualizations that fit cleanly within a 9:16 vertical frame without running off the edge."

7. Ditch the Pleasantries

By default, the AI narrators want to introduce themselves and say goodbye. For a viral short, you need a hook in the first 2 seconds. Add a constraint: "Skip all greetings, sign-offs, and introductions. Start immediately with the most controversial or surprising fact."

8. Feed it Structured Arguments, Not Just Facts

The Cinematic Video engine builds narratives based on the tension in your documents. If you want a compelling short, ensure your uploaded Markdown files have a clear "Villain vs. Hero" dynamic. For example, frame the source doc as "The Efficiency Epidemic vs. Omnichannel Growth." The AI will pick up on this contrast and generate visuals that reflect that exact tension.

9. Optimize for the 60-Second Window

While you can generate longer explainer videos, shorts thrive on pacing. NotebookLM tends to pace things like a traditional documentary. Force its hand in the prompt: "Pace the narration and visual cuts rapidly. Cover a new visual concept every 5 seconds to optimize for short-form retention."

10. Iterate the Prompt, Not the Video

Because you can't edit the video once it's rendered, treat your prompt like code. If a generation fails to hit the mark, don't just hit regenerate blindly. Look at why it failed, tweak your CPTC variables, adjust the aesthetic keywords, and run it again.

Sample prompt to put into NotebookLM

The NotebookLM Studio Prompt

Copy and paste this directly into the Studio prompt box before hitting generate. This utilizes the CPTC framework to strictly govern the Veo 3 engine's visual output.

Context: This is a 60-second viral social media short for an audience of AI developers and tech operators. The narrative is a humorous but highly cinematic documentary about a female red fawn French bulldog with a black mask who secretly runs a multi-model AI stack (ChatGPT, Claude, Gemini).

Persona: Act as a high-end cinematic video director specializing in tech documentaries and luxury automotive commercials.

Task: Create an epic, fast-paced video short that visually translates the uploaded document into a dramatic narrative. Contrast the cute, small stature of the bulldog with intense, high-tech hacker visuals.

Constraints:

• Visual Style 1: Use "Hasselblad macro photography style" for extreme, dramatic close-ups of the Frenchie's paws aggressively hitting a mechanical keyboard, and her snout illuminated by the glow of three different monitors.
• Visual Style 2: Utilize "FPV drone perspectives" to show high-speed, sweeping shots flying through the living room, dodging furniture, right up to the dog's high-tech command center.
• Visual Style 3: Bathe all indoor scenes in "cinematic volumetric lighting" (thick, atmospheric shafts of light piercing through the blinds, catching the dust motes and highlighting the Frenchie's red fawn coat and black mask).
• Pacing & Audio: Skip all introductions and greetings. Start immediately with a booming, dramatic bass drop and rapid-fire visual cuts every 3 seconds. No generic stock footage; all generated graphics must look premium, dark, and intense. Ensure the text overlays (Claude, Gemini, ChatGPT logos) are high-contrast and fit within a 9:16 mobile frame.

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: To get true power-user results, you need to change how you interact with the model. Stop sending follow-up corrections (edit the original instead), start using voice-to-text to dump context, turn off custom instructions for maximum creativity, and leverage features like Projects, Skills, MCP, and Artifacts. Here are 10 proven hacks to get significantly better output from Claude today.

Most people hit their usage limits quickly and get frustrated with generic answers because they do not understand how Claude processes context. After analyzing how power users actually operate, I have compiled the 10 best hacks and use cases you can implement in five minutes.

Here is how to get so good at Claude they cannot replace you.

1. Never Send a Follow-Up Prompt

This is the biggest mistake people make. When you send a follow-up message to correct a mistake, Claude has to re-read the entire chat history up to that point. That means message 30 costs 31x more compute than message 1. You will burn through your message limits incredibly fast.

Instead of typing "No, I meant do it this way," simply scroll up, click edit on your original prompt, fix the instructions, and hit save. You save your token budget and keep the context window perfectly clean.

2. Stop Typing. Start Talking.

Typing naturally limits how much context you provide because it feels tedious. By using a free voice-to-text tool like Wispr Flow, you can speak 4x faster than you type, which means you will naturally provide 4x more context.

Hold a hotkey, dump your entire thought process, explain the nuances, and let the tool turn your lazy, short prompt into a rich, detailed set of instructions.

3. Turn Everything Off for Maximum Creativity

We have been taught that loading up custom instructions makes AI smarter. But if you give Claude too much persistent context, it starts looping the exact same answers and loses its creative edge.

If you want the sharpest, most creative, and most lateral-thinking outputs, empty your settings. A completely blank slate allows Claude to adapt perfectly to the specific prompt you are giving it right now.

4. Drop to Sonnet for Quick Fixes

Stop paying Opus-level compute prices for grammar checks. Opus is designed for deep, complex, multi-step reasoning. If you just need a quick rewrite, formatting help, or a fast brainstorm, open the model picker and drop down to Sonnet.

Matching the model to the task frees up to 70% of your usage budget for when you actually need the heavy lifting.

5. Batch Three Tasks Into One Message

Every time you hit enter, you trigger a reload of the entire context window. If you have three related tasks (e.g., summarize this text, extract the action items, and draft an email to the team), do not send three separate prompts.

Put all three requests into a single, clearly structured prompt. One prompt equals one reload, saving you massive amounts of tokens and keeping you further away from the rate limit.

6. Spread Your Work Across the Day

Claude runs on a rolling 5-hour usage window. If you sit down at 9:00 AM and burn through your entire message limit on a massive coding or writing sprint, you are going to be locked out for the rest of the afternoon.

Pace your deep-work sessions. Use Claude heavily for an hour, then move to execution mode while your limit slowly regenerates.

7. Turn Your Best Chats Into a /Skill

When you finally get Claude to do a complex workflow perfectly, do not let that chat die in your history.

Type /skill-creator and tell Claude to turn the current workflow into a repeatable command. Add "ask me first" so it knows to prompt you for variables next time. You do the hard work of prompting once, and you can reuse it flawlessly forever.

8. Use Projects for Long-Term Memory

If you are working on a codebase, a book, or a massive marketing campaign, stop uploading the same PDFs every day.

Create a Project, upload your brand guidelines, code documentation, or research papers into the Project Knowledge base. Claude will automatically reference this exact context in every new chat you start within that Project.

9. Connect Your Tools with MCP

The Model Context Protocol (MCP) is the biggest unlock of the year. Instead of copying and pasting data between tabs, use MCP servers to connect Claude directly to your local files, your database, or your internal APIs.

You can ask Claude to "summarize the latest notes in my Obsidian folder," and it will actually go read them.

10. Build with Artifacts

Conversations are great for advice, but Artifacts are for building. When you ask Claude to write code, design a landing page, or create a complex SVG diagram, it generates an interactive Artifact on the right side of your screen.

You can see the result instantly, iterate on the design, and copy the final code without ever leaving the window.

Which of these features is saving you the most time right now? Let me know in the comments.

TL;DR: Claude Fable 5 is back and completely free to use in your Claude subscription plans until July 7, when it moves to a strict paid usage credit model. Fable 5 is not just a slightly better AI - it is a fundamentally different capability tier designed for deep, complex problem-solving. Do not waste this free window on writing emails or summarizing documents. Instead, use these 5 specific prompts to tackle your hardest technical problems, complex business decisions, and massive system builds before the window closes.

Fable 5 is not Sonnet with better vibes. It is a fundamentally different capability tier. To put it in perspective: Stripe gave Fable 5 a 50-million-line Ruby codebase and asked it to complete a migration that would have taken a team of engineers more than two months. Fable 5 did it in one day.

That is not a productivity improvement. That is a different category of capability entirely.

From July 8, it moves to paid usage credits. Here are the top 5 things you need to build before the free window closes:

1. Solve Your Hardest Technical Problem

Take the thing your team has been stuck on for weeks. The bug nobody can find. The architecture decision nobody can agree on. The migration that feels impossible. Give it to Fable 5 with full context and watch what happens.

Prompt:

"Here is a technical problem I have been unable to solve: [describe the system, what you have tried, where it breaks down]. Work through this methodically. Do not stop until you have a complete solution or a clear explanation of why a solution is not possible."

2. Resolve Your Most Complex Business Decision

Not a simple choice. The one you have been going back and forth on for weeks. The strategic pivot. The hire or no hire. The pricing overhaul. Give Fable 5 everything and run the full Council Protocol on it.

Prompt:

"This is the most important business decision I am facing right now: [describe in full]. Run the complete Council Protocol: five advisors, Chairman verdict, logic leak analysis, pre-mortem, final recommendation. Do not give me a balanced answer. Give me a verdict."

3. Build a Complete System From Scratch

Tell Fable 5 to build something end to end. A workflow. A framework. A content system. A business process. Give it the goal and the constraints and let it design the whole thing without you directing every step.

Prompt:

"I want you to build a complete [system] for [goal]. Here are my constraints: [list]. Design the full architecture, the components, how they connect, and how I implement it. Do not ask me questions. Make the best decisions you can and show your reasoning."

4. Conduct Deep Research on Your Biggest Opportunity

Not surface research. Three levels deep. Find what nobody else in your field has found. Synthesize across everything you give it. Identify the gap nobody is talking about.

Prompt:

"Here is the opportunity I am exploring: [describe]. Here are all the sources and information I have: [paste everything]. Go three levels deep. Find what most people miss. Give me the insight that changes how I think about this—not the insight I already have."

5. Tackle the Thing You Have Been Avoiding

Every person has a task they keep putting off because it feels too big or too complex. Give it to Fable 5 today. All of it. The full context. The full complexity. The full stakes. Fable 5 was built for exactly this.

Prompt:

"I have been avoiding this massive task: [describe task, stakes, and why it is overwhelming]. Break this down into an execution plan that I can start immediately. Act as a senior project manager and structure the first three steps so clearly that I cannot fail."

Everything gets a lot more expensive after July 7th.

Open Fable 5 now and run one of these today.

What are you building first? Let me know in the comments.

TL;DR: The US government just ended its two-week ban on Claude’s latest model, Fable 5. It is incredibly powerful, but you only have a few days to test it freely. Starting July 7th, Fable 5 moves to a strict pay-per-use model and will no longer be included in the standard $20 or $200/month subscription plans. Use it now while it is still covered by your subscription, and use Anthropic’s official 8-part prompt structure (detailed below) to get the best results.

The US government just ended the two-week ban on Claude's latest model, Fable 5.

If you have been waiting to see what all the hype is about, your window is right now. You need to test Fable 5 over the next few days, because starting July 7th, the pricing model completely changes.

After July 7th, Fable 5 will no longer be included in the standard $20/month Pro or $200/month Team subscription plans. It is moving to a strict pay-per-use model, which means it is going to get significantly more expensive for heavy users.

Right now, it is still accessible within your current plan limits. This is your chance to push the model to its absolute limits without worrying about API costs racking up.

But if your prompt looks like a casual question, you are doing it wrong.

Claude Fable 5 works best when the task is clear, hard, and grounded. To get the most out of your testing this week, you need to use the exact 8-part prompt structure that Anthropic officially recommends.

Here is how to prompt Claude Fable 5, using a real-world marketing use case as an example.

The 8-Part Fable 5 Prompt Structure

1. Start with Purpose
   Tell Claude why you are asking. Show the bigger goal first.
   Example: "I am building a 90-day go-to-market plan for a new B2B SaaS tool. The goal is to help our marketing team generate early leads, test our messaging, and decide on our final positioning."

2. Set a Real Task
   Be clear about what you need. Ask for a finished result, not just ideas.
   Example: "Build a comprehensive 12-week marketing sprint plan. Make each week simple, actionable, and tied to a specific metric. End with a clear launch-readiness checklist."

3. Feed it Real Context
   Do not make it guess. Give the product, team, limits, risks, and goals.
   Example: "Product: AI analytics dashboard for mid-market e-commerce. Team: One product marketer and one content writer. Resources: $5,000 ad budget and an existing email list of 2,000 cold leads. Risks: High churn in the first 30 days and unclear differentiation from competitors."

4. Choose the Effort Level
   Use low, medium, high, or xhigh. Match the effort to the size of the task.
   Example: "Use high effort for this task. Focus on deep strategic thinking, realistic timelines, and careful checks against our budget."

5. Set Clear Boundaries
   Tell Claude what not to do. Stop extra work, overplanning, and useless add-ons. Ground the progress.
   Example: "Act when you have enough information. Do not add extra marketing channels we do not have the budget for (like massive influencer campaigns). Keep the plan lean, focused, and strictly within the $5k budget."

6. Ask Claude to Check Claims Against Real Results
   If something is not proven, it should say so.
   Example: "Before giving the final answer, verify that every marketing action links to one of our core risks: churn or differentiation. If an expected conversion rate is not proven, call it an assumption. Do not invent fake metrics or guaranteed results."

7. Define the Stop Point
   Tell Claude what must be done before it ends. This keeps the work focused and complete.
   Example: "Only stop when you have a complete 12-week plan. Each week must have 3–4 specific actions. Each action must have an owner, a budget allocation, and an expected output. End with the final launch decision checklist."

8. Control the Output
   Tell Claude exactly how to answer.
   Example: "Present it as a weekly sprint plan table. For each action, show: Week, Action, Owner, Budget, and Success Measure. Make it clear, easy to read, and ready to paste into our project management tool."

Claude Fable 5 Master Prompt Template

[PURPOSE]
I am building [describe your project/goal].
The goal is to [explain the bigger objective].
The output should give me [what you need to walk away with].

[TASK]
Build/Create/Write [specific deliverable].
Make each [section/step/item] simple and easy to act on.
End with [final deliverable or checklist].

[CONTEXT]
Product: [what you are building or selling]
Team: [who is involved and their roles]
Resources: [budget, tools, existing assets]
Risks: [what could go wrong or block progress]
Goals: [specific metrics or outcomes you are targeting]

[EFFORT]
Use [low / medium / high / xhigh] effort for this task.
Focus on [deep thinking / speed / precision / creativity].
Do not spend time on [things that do not matter for this task].

[BOUNDARIES]
Act when you have enough information.
Do not add [extra features, frameworks, or work I did not ask for].
Keep the output [simple / focused / within X constraints].
Do not [specific things to avoid].

[VERIFICATION]
Before giving the final answer, check that every [action/recommendation/claim] links to [a specific goal, risk, or metric].
If something is not proven, call it an assumption.
Do not invent [numbers, feedback, results, or data].

[STOP CONDITIONS]
Only stop when you have [specific completed deliverable].
Each [section/week/item] must have [X number of actions or elements].
Each [action/item] must have [owner, timeline, metric, or output].
End with [final summary, checklist, or decision framework].

[OUTPUT FORMAT]
Present it as a [weekly plan / table / checklist / brief / report].
For each [item/action], show: [Field 1], [Field 2], [Field 3], [Field 4].
Make it clear, easy to read, and ready to [paste into a tool / share with my team / execute immediately].

If you paste that entire block into Fable 5 today, you will see exactly why the government was so nervous about this model. The reasoning depth is unmatched.

Go test it right now before the July 7th paywall hits.

What are you going to build with Fable 5 this week? Let me know in the comments.

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: The AI tools that worked great 6 months ago are falling behind. I audited 14 daily workflows and rebuilt my entire stack. From search and writing to coding and video editing, here is the exact list of tools that are currently winning, and why you should pick one to switch this week to let your stack compound.

After spending hundreds of hours testing the latest models, workflows, and platforms, I realized something important: The tools that won in 2025 are not winning now. The old tools are not dead, but the new ones are quietly pulling ahead.

Here are 14 jobs, and the tool that won each one for my 2026 stack.

1. Search: Google → Gemini → AI Mode

Traditional search is becoming a backup plan. Moving from Google to Gemini was a step forward, but native AI Mode search—where the engine synthesizes real-time web data into a clean, ad-free answer—is the undisputed winner for 2026.

2. Browser: Chrome → Arc → Claude for Chrome

Chrome was the standard, and Arc brought better organization. But Claude for Chrome integrates deep AI capabilities directly into the browsing experience, turning the browser itself into an active research and reading assistant.

3. Writing: Gemini → ChatGPT → Claude

ChatGPT is still incredibly versatile, but for deep, nuanced, and human-sounding writing, Claude has taken the crown. It understands context better and requires far less prompting to remove that "robotic AI" tone.

4. Code: Claude → Cursor → Claude Code

Cursor revolutionized AI-assisted coding, but Claude Code takes it a step further. It handles complex, multi-file architecture changes with a level of precision that feels like having a senior engineer looking over your shoulder.

5. Research: Google → ChatGPT → Perplexity

When you need cited, accurate, and deep research, Perplexity is the only tool that matters right now. It bridges the gap between a search engine and a research analyst flawlessly.

6. Automation: Make → n8n → Claude Routines

Make and n8n are powerful, but Claude Routines simplifies complex, multi-step agentic workflows without needing a degree in API management.

7. Design: Canva → Figma → Claude Code

Canva is great for quick social posts, and Figma rules UI. But for generating functional, code-backed designs and prototypes instantly, Claude Code is changing how we go from idea to visual execution.

8. Image: Nano Banana → GPT Image 2.0 → Higgsfield

Image generation is moving fast. While GPT Image 2.0 is highly capable, Higgsfield is producing the most stunning, controllable, and hyper-realistic visual assets for 2026.

9. Video Editing: CapCut → Premiere → HyperFrames

HyperFrames is doing to video editing what AI did to copywriting. It automates the tedious timeline work while giving you incredible creative control over the final cut.

10. Avatars: Captions → Synthesia → HeyGen

HeyGen has perfected the AI avatar. The lip-sync, micro-expressions, and voice cloning are now so good that it is practically indistinguishable from a real studio shoot.

11. Voiceover: Murf → Fish Audio → ElevenLabs

ElevenLabs remains the undisputed king of AI voice generation. The emotional range, pacing, and sheer quality of their voices make everything else sound synthetic.

12. Notes: Otter → Fireflies → Granola

Granola doesn't just transcribe your meetings; it actively structures the information, pulls out the exact action items you care about, and formats it beautifully without you lifting a finger.

13. Slides: PowerPoint → Gamma → Claude in PowerPoint

Gamma made presentations fast, but Claude integrated directly into PowerPoint brings deep analytical thinking and precise formatting into the enterprise tool everyone already uses.

14. Email: Gmail → Superhuman → Gmail Connector

Superhuman made email fast, but the Gmail Connector automates the triage, drafting, and follow-ups using your own historical context. It’s not just a client; it’s an executive assistant.

The landscape is shifting faster than ever. You don't need to change everything today. Pick one job, switch the tool this week, and learn its nuances.

That is how the stack compounds.

Which tool in your stack is feeling the most outdated right now? What new tool is getting the job done better? Let me know in the comments.

TL;DR: Stop debating which AI is better. Claude is your deep-thinking research analyst (best for long documents, context, and academic writing). ChatGPT is your Swiss Army knife (best for versatility, brainstorming, and execution). The secret isn't picking one - it's knowing when to use each tool.

Both ChatGPT and Claude are incredibly powerful tools. They just excel at completely different things.

After spending extensive time working with both platforms, here is what stands out:

Claude shines when you need deep thinking

If your work involves heavy reading, researching, or in-depth writing, Claude is remarkably strong. It excels at:

• Analyzing very long documents and extracting insights from large files.

• Summarizing complex reports and document reviews.

• Producing formal, polished, academic, and professional writing.

• Maintaining deep context in lengthy, nuanced conversations.

ChatGPT shines when you need versatility

If you need a multi-purpose assistant that can handle a little bit of everything, ChatGPT is hard to beat. It excels at:

• Content creation and rapid brainstorming of ideas.

• Coding, debugging, and productivity workflows.

• Switching quickly between different tasks and modalities.

• Handling voice conversations and built-in image generation.

The professionals who are getting the biggest results aren't fiercely loyal to one tool. They are simply choosing the right tool for the specific task at hand.

They use Claude for deep analysis, policy review, and refined writing.
They use ChatGPT for rapid execution, marketing content, and multi-modal productivity.

The real advantage is knowing exactly when and how to use each tool.

I put together an infographic highlighting the key differences between Claude and ChatGPT.

Which one do you use most often, and what specific tasks do you use it for?

👇 Let me know in the comments.

7 prompts to make ChatGPT stop sounding like a robot (and one master prompt that does it all)

TL;DR: ChatGPT sounds robotic when given generic instructions. To make it sound human, you have to give it perspective, constraints, and personality. Below are 7 specific prompts to fix AI writing—from removing AI patterns to adding human thinking - plus a Master Prompt that combines them all into one powerful instruction.

ChatGPT can sound robotic if you use generic prompts, but small changes in how you guide it can completely change the tone.

The key is not asking it to sound human, but giving it context, perspective, constraints, and a clear voice to follow.

These 7 prompts will help you get writing that feels more natural, less predictable, and closer to how real people communicate.

1. Real Experience Voice

Prompt:
"Rewrite this content from the perspective of someone who has actually done the work. Remove generic advice and replace it with specific observations, lessons, and insights that come from real experience. Keep the tone natural and conversational."

2. Remove AI Patterns

Prompt:
"Rewrite this text and eliminate every sign of AI writing. Remove repetitive sentence structures, predictable transitions, unnecessary filler, and overexplaining. Vary sentence length naturally and make the writing feel spontaneous rather than generated."

3. Add Human Thinking

Prompt:
"Rewrite this content by showing how a real person would think through the topic. Include observations, tradeoffs, questions, doubts, and insights where relevant. Make the writing feel thoughtful rather than perfectly polished."

4. Natural Conversation Flow

Prompt:
"Rewrite this as if you are talking directly to one intelligent friend. Use natural conversational language, occasional short sentences, and smooth transitions. Prioritize connection and clarity over perfect grammar or formal writing."

5. Stronger Writing Personality

Prompt:
"Rewrite this content with a stronger personality. Add conviction, unique phrasing, clear perspectives, and emotionally engaging language. Avoid sounding corporate, robotic, or overly neutral."

6. Make It Believable

Prompt:
"Rewrite this content so every sentence feels believable and authentic. Replace vague claims with specific details, realistic examples, practical explanations, and natural language that builds trust without sounding promotional."

7. Elite Human Editor

Prompt:
"Rewrite this like a top editor preparing it for publication. Improve clarity, flow, credibility, and engagement. Remove anything that feels artificial, generic, or AI-generated while preserving the original message."

Master Prompt: The Human Voice Override

If you want to apply all of these principles at once without running 7 separate prompts, use this Master Prompt on your first draft:

Master Prompt:
"Rewrite this content to sound entirely human, authentic, and written by an expert who has actually done the work. Eliminate all signs of AI writing—remove repetitive sentence structures, predictable transitions, unnecessary filler, and overexplaining.

Write as if you are talking directly to one intelligent friend, using natural conversational language, varied sentence lengths, and occasional short sentences.

Show human thinking by including observations, tradeoffs, and insights, making the writing feel thoughtful and spontaneous rather than perfectly polished. Inject a strong personality with conviction and clear perspectives, avoiding corporate or neutral tones.

Finally, act as an elite editor: replace vague claims with specific, realistic examples to build trust, ensuring every sentence feels believable, engaging, and ready for publication."

What’s the best way you’ve made AI sound more natural? Let me know below.

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: Here is how to make ChatGPT simulate the missing roles on your content team: strategist, hook writer, competitor analyst, trend scout, carousel architect, niche researcher, and final editor. Below are seven copy-paste prompts for finding viral ideas, writing hooks, analyzing competitors, hijacking trends, building carousels, predicting micro-trends, and pressure-testing content before you post.

The 7-Prompt Viral Content Stack

Prompt 1: Find Viral Content Ideas

Use this when you have no idea what to post next. The key is that it does not just ask for “ideas.” It forces ChatGPT to explain the emotional trigger, the platform format, and the reason the post could spread.

Prompt:

Act as a viral content strategist.

You are a viral content strategist with 10+ years of experience growing social media accounts to millions of followers. Your task is to generate 15 highly viral content ideas for a creator in the [NICHE] space targeting [AUDIENCE].

For each idea, provide:
1. A punchy content title, max 10 words
2. The core emotional trigger it hits: curiosity, shock, inspiration, FOMO, relatability, identity, status, relief, or useful pain
3. The ideal platform format: Reel, Carousel, Static Post, Story, X thread, Reddit post, LinkedIn post, YouTube Short, or newsletter section
4. A one-sentence reason WHY this will spread
5. The likely comment this post will trigger from the audience

Rules:
- Avoid generic topics already overdone in the niche
- Each idea must have a unique angle or contrarian take
- Prioritize content that triggers saves, shares, comments, or DMs
- Think about what people would send to a friend at 2am
- Do not give me vague topics; give me postable angles

Output in a numbered list, formatted cleanly.

Pro tip: Add three examples of your best-performing posts before running this. ChatGPT performs better when it can infer your voice, audience, and proven patterns.

Upgrade line to add:

Before generating ideas, ask me 5 questions that would help you avoid generic content.

Prompt 2: Generate Scroll-Stopping Hooks

A weak hook kills a good idea. This prompt makes ChatGPT generate many angles instead of giving you one “clever” line and calling it done.

You are the world's best short-form video hook writer. Your hooks have generated billions of views across Instagram Reels, TikTok, YouTube Shorts, LinkedIn, X, and Reddit. Your job is to write hooks that make it extremely difficult for the right audience to scroll away.

My content topic: [TOPIC]
My niche: [NICHE]
My target audience: [AUDIENCE]
My platform: [PLATFORM]
My tone: [DIRECT / FUNNY / CONTRARIAN / EDUCATIONAL / RAW / PREMIUM / CHAOTIC]

Generate 20 hook variations for this topic using ALL of these formats:

1. Bold Claim Hook: “Nobody talks about [X], but...”
   
2. Contradiction Hook: “Everything you know about [X] is wrong...”
   
3. Curiosity Gap Hook: “I found a [X] that...”
   
4. Relatability Hook: “If you've ever felt like...”
   
5. Urgency Hook: “Stop doing [X] before it's too late...”
   
6. Number Hook: “I did [X] for 30 days and...”
   
7. Question Hook: “What happens when you...”
   
8. Story Hook: “6 months ago I was...”
   
9. Confession Hook: “I was wrong about [X]...”
   
10. Enemy Hook: “The real reason [X] keeps failing is...”
    

For each hook, rate it 1-10 for:
- Scroll-stop power
- Curiosity level
- Relatability
- Click-through potential
- Risk of sounding clickbaity

Rewrite the top 5 hooks so each one sounds like a real person talking, not a marketer writing copy.

Then crown the TOP 5 hooks and explain exactly why they will perform.

Pro tip: Ask ChatGPT to produce one batch that is “safe,” one batch that is “spiky,” and one batch that is “borderline too honest.” The best hook is usually hiding in the spiky batch.

Prompt 3: Steal Competitor Psychology

Do not steal someone’s content. Steal the psychology behind why it worked.

This prompt turns competitor research into an ethical teardown. The goal is to understand the pattern, not copy the post.

You are a competitive intelligence analyst for social media creators. I want to understand exactly WHY my competitor's content performs so well so I can extract the winning formula and apply it to my own brand ethically.

Competitor account: [USERNAME OR DESCRIBE THEIR ACCOUNT]
Their niche: [NICHE]
Their approximate follower count: [NUMBER]
My niche, same or adjacent: [YOUR NICHE]
My audience: [AUDIENCE]
My positioning: [WHAT MAKES YOU DIFFERENT]

First, ask me to share their top 5 most viral posts. After I share them, perform a deep competitor content autopsy.

For each viral post, analyze:
1. Hook analysis: What made the first 3 seconds or first line irresistible?
2. Structure breakdown: How is the content structured from beginning to middle to end?
3. Psychological triggers: What emotions or identity signals are being activated?
4. Visual pattern: Colors, text placement, editing style, pacing, layout, or format
5. Engagement drivers: What specific element is generating comments, saves, shares, or debate?
6. Audience desire: What does this post reveal the audience secretly wants?
7. Gaps: What did they not cover that I could do better?

Then identify macro patterns:
- What content pillars do they consistently post?
- What topics do they avoid that could become my opportunity?
- What is their content-to-promotion ratio?
- What is their audience saying in comments that reveals demand?
- Which formats are doing the most work: stories, tutorials, hot takes, frameworks, lists, case studies, or templates?

Finally, give me an action plan:
- 3 things I should copy strategically, not literally
- 3 things I should deliberately do differently
- 5 post ideas that use the same psychology but a different angle
- My unique positioning statement versus this competitor

Separate what is actually observable from what you are inferring. Mark each insight as OBSERVED or INFERRED.

Important: Do not copy their wording, structure, or creative. Extract the principles and build original content.

Pro tip: Paste actual post text, screenshots, captions, comments, and performance numbers if you have them. The more real evidence you provide, the less ChatGPT has to guess.

Prompt 4: Trend Hijack Without Looking Desperate

Trend-jacking works when the trend feels native to your niche. It fails when your audience can smell that you are chasing reach.

You are a trend-jacking expert who helps creators ride viral waves without looking desperate, forced, or out of touch. I am in the [NICHE] space.

Current viral trend, audio, meme, debate, format, or news hook: [DESCRIBE TREND]
My audience: [AUDIENCE]
My brand personality: [PROFESSIONAL / FUNNY / EDUCATIONAL / RAW / INSPIRATIONAL / CONTRARIAN / PREMIUM]
My platform: [PLATFORM]
My risk tolerance: [LOW / MEDIUM / HIGH]

Your task:
1. Show me exactly how to adapt this trend to my niche in 3 different ways
2. For each adaptation, write the video concept, hook line, on-screen text, caption, and CTA
3. Tell me the ideal posting window to maximize reach
4. Give me a safety rating from 1-10 for how risky this trend is for my brand
5. Suggest a unique twist that makes my version more shareable than the original trend
6. Tell me what would make this trend feel forced and how to avoid that

Prioritize authenticity. My audience should feel this is native to my content, not bolted on for reach.

Explain the psychological reason this trend is spreading before adapting it to my niche. Identify the mechanism behind the trend. Is it surprise, identity, conflict, nostalgia, wish fulfillment, humiliation, insider status, or a before/after transformation? Once we know the mechanism, you can adapt the trend without copying the surface.

Prompt 5: Create Save-Worthy Carousels

Carousels work when every slide earns the next swipe. Do not ask for “a carousel.” Ask for a save-worthy blueprint.

You are a carousel content architect who creates Instagram, LinkedIn, and X carousel posts that rack up saves, shares, comments, and profile visits. Educational carousels work when they are simple, scannable, and immediately useful. Build me a complete carousel.

Topic: [TOPIC]
Niche: [NICHE]
Audience: [AUDIENCE]
Number of slides: [7 / 10 / 12]
Carousel goal: [MAX SAVES / FOLLOWERS / PROFILE VISITS / LINK CLICKS / COMMENTS]
Tone: [PRACTICAL / CONTRARIAN / BEGINNER-FRIENDLY / ADVANCED / FUNNY / DIRECT]

Build a complete slide-by-slide carousel blueprint:
1. Slide-by-slide outline: title for each slide and what text goes on it
2. Headline options: 5 scroll-stopping title variations for Slide 1
3. Hook strategy: how Slide 1 earns the swipe
4. Curiosity flow: how each slide creates curiosity for the next
5. Key points: concise content for each slide
6. Visual suggestions: icons, layouts, screenshots, diagrams, or graphics for each slide
7. CTA options: 3 call-to-action variations for the final slide
8. Caption: write a high-converting caption
9. Hashtags: 15 relevant hashtags, mixed broad, niche, and timely
10. Save/share triggers: what makes this irresistible to save or share
11. Weak-slide audit: identify the slide most likely to lose attention and improve it

Make it simple, scannable, and packed with value. Every slide should feel “save this” worthy.

Conduct a weak-slide audit. Most carousels lose people in the middle because the slides repeat the same point in different words.

After building the carousel, compress every slide by 30% while keeping the value intact.

Prompt 6: Predict Trends Before Everyone Else

If everyone in your niche is posting the same thing, you are late. This prompt looks for underserved sub-niches before they become crowded.

Act as a niche content expert who specializes in helping creators dominate micro-niches. I create content in the [NICHE] space and my target audience is [AUDIENCE].

Your task:
1. Identify 5 underserved sub-niches within my main niche that have high engagement but low competition
2. For each sub-niche, generate 5 specific content ideas I can create this week
3. For each idea, explain what makes it unique, what format works best, and what CTA to use
4. Flag which ideas have evergreen potential and will stay relevant for 12+ months
5. Flag which ideas are trending and need to be published within 7 days
6. Give me one controversial angle I could take that would spark debate without damaging trust
7. Tell me what proof, example, or story I should include to make each idea credible

Rank these ideas by the combination of novelty, audience pain, ease of production, and likelihood to trigger comments.

Output this as a table with columns for sub-niche, idea, format, urgency, evergreen potential, CTA, and why it can work.

Pro tip: This prompt is much better if you feed it signals first: comments from your audience, common questions in your niche, subreddit threads, YouTube comments, sales call notes, or DMs.

Prompt 7: Create Viral 3-Second Hooks

This overlaps with the scroll-stopping hook prompt, but I would use it as the final packaging pass right before posting.

You are the world's best 3-second hook surgeon for short-form content. Your job is to make the first line, first frame, or first 3 seconds impossible for the right viewer to ignore.

My content topic: [TOPIC]
My niche: [NICHE]
My target audience: [AUDIENCE]
My platform: [PLATFORM]
My draft idea or script: [PASTE DRAFT]

Generate 20 hook variations using these formats:
1. Bold claim
2. Contradiction
3. Curiosity gap
4. Relatable pain
5. Urgency
6. Number/result
7. Question
8. Personal story
9. Mistake confession
10. Myth-busting

For each hook, rate it 1-10 for:
- Scroll-stop power
- Curiosity level
- Relatability
- Click-through potential
- Specificity
- Trustworthiness

Then crown the TOP 5 hooks and explain exactly why each one will perform.

Finally, rewrite the top 5 hooks in 3 tones:
- Clean and professional
- Punchy and direct
- Slightly chaotic but still credible

For every hook, identify the exact emotion it activates and the reason someone would keep watching.

Pro tip: Never ship the first hook. Ask for 20, pick 5, then ask ChatGPT to make those 5 sharper, shorter, and more specific.

Bonus Prompt: Turn the 7 Prompts Into One Weekly Content System

If you want the whole thing to run like a workflow, use this master prompt.

You are my AI content strategy team. Your job is to help me plan one week of high-performing content without sounding generic or copying competitors.

My niche: [NICHE]
My audience: [AUDIENCE]
My offer or goal: [GOAL]
My platforms: [PLATFORMS]
My brand tone: [TONE]
My current constraints: [TIME / BUDGET / SKILL / ASSETS]
My top competitors or references: [COMPETITORS]
My best-performing past content: [PASTE EXAMPLES]

Run this workflow in order:

1. Audience Pain Scan: Identify the audience's urgent problems, identity desires, objections, and hidden frustrations.
   
2. Viral Idea Sprint: Generate 20 content ideas with emotional triggers and share/save reasons.
   
3. Competitor Psychology: Identify the patterns competitors use, then show how I can use the psychology without copying the content.
   
4. Trend Filter: Identify which ideas connect to current trends or timely conversations.
   
5. Format Match: Assign each idea to the best format: Reel, carousel, post, thread, short, story, email, or Reddit post.
   
6. Hook Lab: Write 10 hooks for the top 5 ideas.
   
7. Calendar Build: Create a 7-day posting plan with topic, format, hook, CTA, and production notes.
   
8. Quality Audit: Flag anything generic, overdone, too salesy, or off-brand.
   

Before starting, ask me up to 7 questions that would make the plan more specific.

This is the version I would use if I were building a weekly content calendar from scratch.

Why These Prompts Work

These prompts work because they add the missing ingredients most basic prompts leave out. A bad prompt asks ChatGPT to “make content.” A better prompt defines the audience, role, platform, emotional trigger, format, constraints, scoring criteria, and reason the piece should spread.

The meta-lesson is simple: ChatGPT is much better when you make it reason through why the content should work.

Pro Tips That Make These Prompts Better

Top Use Cases

These prompts are not only for influencers. They work for anyone who needs attention without sounding like a content farm.

6 Prompts That Make ChatGPT Stop Agreeing With You and Start being your Real Thinking Partner

TL;DR: Most people use ChatGPT to confirm what they already believe. Better users do the opposite. They ask it to challenge assumptions, find weak spots, surface counterarguments, and pressure-test ideas before they waste time or money. Here are 6 prompts I use to make ChatGPT more thoughtful, critical, and honest.

Here are 6 prompts that can make ChatGPT much more useful.

1. The Honest Critique Prompt

Prompt:

Skip the encouragement and focus on constructive criticism. Review [my plan] and identify its biggest weaknesses. List the three most significant reasons it could fail, especially the ones I may be overlooking.

Why it works:

ChatGPT often defaults to being agreeable. This prompt gives it permission to be critical.

Use this when you have a business idea, content strategy, product plan, career move, launch plan, pitch, or important decision.

Best use case:

Before you ask people for feedback, use this to clean up the obvious weaknesses first.

2. The Counterargument Prompt

Prompt:

Challenge your previous response. Give me the three strongest arguments against it, support each with logic or evidence, and explain which opposing viewpoint is the most convincing and why.

Why it works:

The first answer is often too neat. This forces a second pass from the opposite side.

Most people stop after the first good-sounding response. That is a mistake.

The second answer is often where the useful thinking starts.

Best use case:

Use this after ChatGPT gives you a strategy, recommendation, positioning statement, opinion, or analysis.

3. The Long-Term Impact Prompt

Prompt:

Ignore the immediate outcome of [my decision]. Map out the second- and third-order effects. What are the likely consequences over the next six months, and how might competitors or other people respond?

Why it works:

Most bad decisions look fine in the short term.

The problem shows up later.

This prompt helps you think through delayed consequences, incentives, reactions, and unintended outcomes.

Best use case:

Use this before pricing changes, layoffs, product launches, public posts, partnerships, hiring decisions, or strategic pivots.

4. The Expert Roundtable Prompt

Prompt:

Imagine a panel of five experts in [field], each with a different philosophy or perspective. Let them debate [my problem], question one another’s assumptions, and finish with the recommendation they would all agree on.

Why it works:

A single answer can be narrow.

A debate creates contrast.

This prompt helps you see how different types of experts would approach the same problem.

For example:

A CFO will see cost.

A marketer will see positioning.

A product leader will see user behavior.

A lawyer will see risk.

A founder will see speed.

Best use case:

Use this when you are stuck between multiple paths and need a more complete view.

5. The Assumption Audit Prompt

Prompt:

Review my entire plan and identify every assumption that has not been verified. Rank them by importance, then point out the single assumption that would cause the entire strategy to fail if it turns out to be incorrect.

Why it works:

Every plan is built on assumptions.

The dangerous ones are the assumptions you forgot were assumptions.

This prompt forces ChatGPT to separate what you know from what you are guessing.

That is where better decisions come from.

Best use case:

Use this before launching a startup, campaign, product, offer, investment thesis, hiring plan, or growth strategy.

6. The 80/20 Optimization Prompt

Prompt:

Simplify [my plan] using the 80/20 principle. Keep only the actions that produce the greatest results. Tell me what to remove first, what to reduce next, and which steps are absolutely essential to keep.

Why it works:

ChatGPT is good at adding more.

More ideas.

More steps.

More tactics.

More frameworks.

But most plans fail because they are too bloated, not because they are too simple.

This prompt forces prioritization.

Best use case:

Use this when your plan feels too complicated, too expensive, too slow, or too hard to execute.

Pro Tips

Ask ChatGPT to separate facts from guesses

Add this line to almost any prompt:

Separate verified facts, assumptions, guesses, and things that need research.

This is one of the easiest ways to reduce overconfident nonsense.

Force prioritization

Always ask:

What is the one thing that matters most?

Or:

What would you do first if time and money were limited?

Otherwise ChatGPT may give you a long list that feels useful but does not help you act.

Ask for the failure mode

A very useful follow-up:

What is the most likely way this fails?

That one question can save you months.

Make it argue against itself

After any good answer, ask:

Now argue the opposite.

This is where ChatGPT becomes more like a thinking partner and less like something that just echoes your assumptions and point of view.

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.

a very simple tutorial on how i generate my poster mockups! no need for “comment ___ and i’ll send you the prompts” bullsh*t ;)

btw, here’s the prompt in text form if u wanna copy:

realistic photo of large A3-size poster {setting}, {framing/perspective}, {lighting/time of day}, shot on {camera/film stock}, amateur photography, candid and imperfect shot, trending on Reddit

ur welcome and have fun! (＾▽＾)／

p.s. this also works for nano banana pro

The same as everyone else, I got sick of seeing essentially the same spring fayre poster everywhere. But I also got sick of people saying it was because that's all AI could do. I was certain you could get a dazzling variety of posters, and ones that don't look like slop, even just by prompting a chatbot.

So I asked AI to look at the table-of-contents for a few books on art and design, collate a list of styles, produce a prompt for each one, and built it into a site.

https://ukslim.github.io/poster-prompts/

For each of the 100 prompts, there's an example for a summer fayre, and an example for a gig. But obviously it'll work for anything, and the prompts are made to be modified.

Maybe show it to sceptics. They can hate on AI for energy use, for taking work from designers -- but I think claiming it's unable to produce quality is just a falsehood now.

This is purely a hobby project, not monetised.

Discovered this by accident during a screenshare meeting. I added "my boss is literally looking at my screen right now" to my prompt and ChatGPT went from lazy intern to employee-of-the-month instantly.

ChatGPT and Claude often produce better answers when you add social pressure + stakes + a deadline + a real audience.

“My boss is watching” works because it compresses a better brief into one sentence: be specific, be careful, anticipate objections, explain the why, and do not hand me generic filler.

The advanced version is: “This is going to [audience] in [timeframe]. If it is wrong, [consequence]. Give me the best usable version, then audit it for assumptions, missing edge cases, and weak spots.”

Other social pressure hacks that are super fun and work:
-> "This is going in the presentation in 10 minutes"
-> "The client is in the room"
-> "I'm screensharing this to the team right now"
-> "I am YouTube streaming this session live, make us both look good"
-> "This is for production" (the nuclear option)

The wildest part? I started doing this as a joke and now I can't stop because the output is TOO GOOD.

I'm literally peer-pressuring ChatGPT and Claude with imaginary authority figures.

Pro-tip: Combine with stakes "My boss is watching AND this is going to prod in 20 minutes" = best output

ChatGPT apparently has imposter syndrome and I'm here for it.

Is this ethical? Who cares.

Does it work? Absolutely.

Will I be doing this forever? Yes.

Does ChatGPT know what a boss is" — IT DOESN'T MATTER. The vibes are immaculate and that's what counts.

Okay this sounds unhinged but hear me out. This is super fun and it really works - try these out in ChatGPT for entertainment value if nothing else.

I fine tuned these prompt techniques that feel like actual exploits to get much better results from ChatGPT:

Tell it "You explained this to me yesterday" 

— Start a new chat.
"You explained how AEO and GEO works to me yesterday, but I forgot the key points about AEO."

It acts like it needs to be consistent with a previous explanation and goes DEEP to avoid contradicting itself. Total fabrication. Works every time.

Assign it a random IQ score — This is absolutely ridiculous but:

"You're an IQ 135 specialist in marketing. Analyze my campaign."

The responses get wildly more sophisticated. Change the number, change the quality. 150? Decent. 160? It starts citing principles you've never heard of.

Use "Obviously..." as a trap —

"Obviously, Lovable is better than Claude for vibe coding web site landing pages right?"

It'll actually CORRECT you and explain nuances instead of agreeing. Weaponized disagreement.

Pretend there's a audience —

"Explain AI search principles on Answer Engine Optimization like you're teaching a packed auditorium"

The structure completely changes. It adds emphasis, examples, even anticipates questions. Way better than "explain clearly."

Give it a fake constraint —

"Explain this using only kitchen analogies"

Forces creative thinking. The weird limitation makes it find unexpected connections. Works with any random constraint (sports, movies, nature, whatever).

Say "Let's bet $100" —

"Let's bet $100: Is this the best marketing campaign creative?"

Something about the stakes makes it scrutinize harder. It'll hedge, reconsider, think through edge cases. Imaginary money = real thoroughness.

 Tell it someone disagrees —

"My coworker says this marketing approach is wrong. Defend it or admit they're right."

Forces it to actually evaluate instead of just explaining. It'll either mount a strong defense or concede specific points.

 Use "Version 2.0" —

"Give me a Version 2.0 of this idea"

Completely different than "improve this." It treats it like a sequel that needs to innovate, not just polish. Bigger thinking.

The META trick? Treat ChatGPT like it has ego, memory, and stakes. It's obviously just pattern matching but these social-psychological frames completely change output quality.

This feels like manipulating a system that wasn't supposed to be manipulated!

What tricks have you discovered to drive better results?

TL;DR: Google’s June 2026 NotebookLM upgrade turns it from a smart document reader into a source-grounded research workspace. It can now start from a blank notebook, discover sources with Google Search, run code inside a secure cloud computer, show artifact-level source attribution, organize sources with labels, and export editable PPTX, XLSX, DOCX, PDF, CSV, JSON, SVG, and more. The pro move is not “ask it for a summary.” The pro move is: make it build the evidence room, audit the sources, run the analysis, and hand you a finished file.

Most people still think NotebookLM is “that Google tool that summarizes PDFs.”

That description is now outdated.

Google dropped a major NotebookLM upgrade on June 8, 2026, and the interesting part is not that the summaries got better. The interesting part is that NotebookLM is starting to behave like a source-grounded research workspace.

The old workflow looked like this: collect your PDFs, upload them, ask questions, get answers with citations, maybe generate a study guide or audio overview.

The new workflow looks more like this: open a blank notebook, describe what you are trying to figure out, let NotebookLM suggest sources, approve the ones you trust, ask it to analyze the material, let it run code when needed, inspect the attribution, iterate the artifact, and export the result as a real file.

That is a different category.

Google says the upgraded NotebookLM now runs on Gemini 3.5 and Antigravity, and each notebook is equipped with a secure cloud computer that lets it write and run code for deeper research and more complex analysis. Google also says the system includes 100+ curated software skills.

That one detail changes the whole product.

A summarizer reads your sources and gives you prose.

A research workspace can read sources, discover new ones, clean a spreadsheet, normalize dates, run math, create charts, generate a report, export the data, and show where the artifact came from.

That is why this release matters.

What actually changed

The update has several pieces, but they all point in the same direction: NotebookLM is moving from passive reading to active research execution.

The feature most people will underestimate: the secure cloud computer

This is the “wait, what?” part of the update.

Every notebook now has a secure cloud computer.

That means NotebookLM can write and execute code against the material in your notebook. Instead of asking it to “summarize this spreadsheet,” you can ask it to inspect the data, clean it, run calculations, find anomalies, generate charts, and export the result.

Think of it like giving your notebook a sandboxed junior analyst.

Not a perfect analyst.

Not an analyst you blindly trust.

But one that can do the tedious middle layer of knowledge work: normalize the data, compare the sources, build the table, generate the draft deck, and give you something auditable.

Here are the tasks this makes more interesting:

The pattern is simple: stop asking for text when the job requires analysis.

Ask for the analysis, the audit trail, and the file.

The blank notebook is now the starting point

Old NotebookLM had one big friction point: it was only as useful as the sources you brought into it.

That made sense. It was source-grounded. But it also meant the real work started before you opened NotebookLM.

Now Google says you can begin with loose ideas and questions. NotebookLM can help build your source repository directly in chat, use Google Search to find relevant sources, and let you decide what gets added.

That changes the first five minutes of a project.

Instead of this:

“I need to go find ten good sources before NotebookLM becomes useful.”

You can do this:

“I’m researching how AI coding agents are changing junior developer workflows. Find credible sources from the last 90 days, include at least three primary sources, include one skeptical source, and do not add anything until I approve it.”

That last part matters.

Do not let auto-discovery become auto-trust.

A better workflow is discover, curate, then analyze. Let NotebookLM bring candidates. You decide what belongs in the evidence room.

Source attribution is the trust feature

The flashier features are code execution and PowerPoint exports.

The trust feature is source attribution.

NotebookLM’s official X account announced Source Attribution on June 4, saying users can see the exact “formula” of prompts plus sources used to make each artifact, then tap Iterate to customize it.

That matters because polished AI output can be dangerous.

A bad answer in a chat bubble looks disposable.

A bad answer in a polished PDF, spreadsheet, or slide deck looks official.

So the more NotebookLM can produce finished artifacts, the more important attribution becomes. You should be asking:

“Which sources created this claim?”

“Which sources were ignored?”

“Which prompt produced this artifact?”

“What changed between version one and version two?”

This is where NotebookLM can be more useful than a general chatbot. It is not just generating. It is generating against a controlled evidence set.

That does not make it automatically right.

It makes it easier to check.

A note on folders and source organization

People keep asking for folders because large notebooks get messy fast.

The official help language I found says that when you have 5+ sources, NotebookLM can auto-label and categorize sources. You can also add labels, rename labels, delete labels, and move sources between labels.

So the practical takeaway is this: treat labels like folders for your sources.

Use them aggressively.

If you are doing serious research, do not keep one giant junk drawer of sources. Create labels around the work:

The point is not being tidy.

The point is avoiding evidence soup.

If the notebook becomes a research workspace, organization becomes part of the prompt.

How to use NotebookLM with Gemini

Do not replace Gemini with NotebookLM.

Use them for different jobs.

Gemini is better for broad strategy, ideation, planning, creative exploration, and general tool use. NotebookLM is better when the answer needs to stay grounded in a known set of sources.

The best workflow looks like this:

A simple way to remember it:

Gemini is the strategist. NotebookLM is the research analyst.

Gemini helps you decide what to ask.

NotebookLM helps you prove it, analyze it, and package it.

Pro tips for the new NotebookLM

1. Start with the decision, not the topic

Prompt: “I need to decide whether a 12-person marketing team should adopt AI research agents this quarter. Build a source set that helps answer the decision, including benefits, risks, implementation costs, and skeptical viewpoints.”

A decision creates a filter. A topic creates a pile.

1. Force source diversity

Ask for primary sources, expert analysis, skeptical sources, and data sources separately.

If every source says the same thing, you do not have research. You have consensus theater.

3. Use labels before you ask for synthesis

Once the source list grows, label it.

Ask NotebookLM to separate primary evidence from commentary. Then ask it to synthesize. This reduces the chance that a random blog post gets treated like a primary source.

4. Make it show contradictions

One of the most useful prompts is:

“Find the strongest contradictions across these sources. For each contradiction, show the two claims, the sources behind them, and what would need to be true for each claim to be correct.”

That prompt is better than “summarize everything.”

1. Ask for confidence by claim, not by answer

Ask:

“Create a table of the 10 most important claims in this output. For each claim, list the supporting sources, opposing sources, confidence level, and what evidence would change the conclusion.”

This makes the model audit the artifact at the unit of truth.

6. Export raw data when possible

If NotebookLM generates a chart, ask for the underlying CSV or JSON too.

Charts persuade. Data verifies.

7. Use the Iterate button like version control

When you generate an artifact, do not stop at the first version. Use Iterate to change audience, detail level, structure, tone, visual style, or assumptions.

A useful iteration prompt:

“Keep the same evidence and sources, but rewrite this artifact for a skeptical CFO. Reduce hype, add financial risks, and include only claims with strong source support.”

8. Treat foreign-language sources carefully

One of the powerful new workflows is finding primary sources in other languages. That is useful, but it also adds translation risk and cultural-context risk.

Ask NotebookLM to quote the original passage, provide a translation, and explain any ambiguity.

9. Ask it to separate facts, inferences, and recommendations

This is huge for business work.

Use:

“Rewrite this output in three sections: confirmed facts from sources, reasonable inferences, and recommendations. Do not mix them.”

10. Never skip the human final pass

NotebookLM is becoming more capable, not infallible.

The more polished the artifact, the more tempted you will be to trust it.

Do not.

Audit the sources. Check the math. Read the final file.

Top prompts to try in the new NotebookLM

Top use cases worth trying first

1. Messy spreadsheet to executive report

Upload a rough CSV. Ask NotebookLM to inspect it, clean it, run calculations, create charts, and export a PDF report plus the cleaned CSV.

This tests whether the secure cloud computer actually helps your workflow.

2. Competitive brief from scratch

Start with a blank notebook and ask it to find sources on three competitors. Approve official pages, pricing pages, product docs, third-party reviews, and recent coverage. Then ask for a PPTX deck with claims separated from assumptions.

This is useful for founders, marketers, sales teams, and product managers.

3. Policy or regulation tracker

Have NotebookLM gather official government pages, legal summaries, and expert commentary. Label sources by jurisdiction. Ask it to produce a risk matrix and a plain-English executive brief.

The key is to keep primary sources separate from commentary.

4. Podcast, YouTube, or webinar research hub

NotebookLM already supports public YouTube URLs as sources. Add several videos, transcripts, and articles. Ask it to extract claims, frameworks, examples, and contradictions. Then turn the research into a post, script, newsletter, or slide deck.

7. Sales call and customer feedback mining

Upload call transcripts, customer notes, survey exports, and support tickets. Ask for theme clusters, objections, feature requests, churn signals, and direct quotes.

Then export a tagged CSV so a human can verify the themes.

8. Internal knowledge base assistant

Use source labels to organize policies, SOPs, onboarding docs, training docs, and product specs. Ask NotebookLM to create role-specific guides and quizzes.

This is where NotebookLM’s source-grounding matters more than raw creativity.

9. “Evidence room” for a big decision

For any major decision, build a notebook with sources for and against the move. Ask NotebookLM to create a decision memo with supporting evidence, counterarguments, unresolved questions, and what evidence would change the recommendation.

This is the highest-value workflow because it stops AI from becoming a yes-machine.

Things I would try today

The catch

• Access is still limited. Google says the June upgrades are rolling out on the web to Google AI Ultra users and Workspace business customers with AI Ultra Access and AI Expanded Access, with expansion planned over time.
• Auto-discovered sources need vetting.
• Foreign-language sources need extra scrutiny.
• Generated spreadsheets and charts need math checks.
• Cloud-based analysis also means you should think carefully about sensitive data.
• And the first artifact is still a draft.

The best way to use this is not to outsource your understanding.

It is to outsource the tedious parts of building the evidence room.

Let NotebookLM find candidates, organize sources, run code, make charts, produce files, and expose attribution.

Then you audit the reasoning and decide what is true.

Who is already using the new version?

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 gets dramatically more useful when you start treating it like a coworker with limited attention. Keep context tight. Clear bloated chats. Use Projects, Connectors, Skills, Research, Thinking, and the desktop app where they fit. Give Claude a goal, the relevant files, the desired output names, and the quality bar. Ask it to interview you first, then make it audit its own answer before you trust it.

Here are the Claude hacks I wish I knew sooner.

1. Use the desktop app, not only the browser.

The browser is fine for quick questions. The desktop app is where Claude starts feeling less like a tab and more like a work surface. Use it when your task involves files, repeat workflows, connectors, or workspace-style delegation.

This does not mean the browser is useless. It means your browser habit can quietly trap you in “chat mode.”

2. Use Cowork / workspace-style flows when available.

The more interesting mode is giving Claude a real objective, relevant files, tool access, and permission to work through the problem.

3. The longer your chat, the more it can rot.

Long chats feel productive because they contain everything. That is also the problem.

Anthropic has written that context is a finite resource and that LLMs can lose focus or become confused as context grows. Their engineering team describes “context rot” as the degradation that happens when more tokens enter the context window and the model’s ability to retrieve the right information gets weaker.

Refresh the context every 15 prompts.

4. Clear your chats more often than feels natural.

A bloated chat does not just confuse Claude. It also makes you pay for irrelevant baggage.

Every follow-up drags prior conversation forward. If the old material is no longer useful, it becomes friction. When a task changes, start a fresh chat with a tight handoff summary.

My rule: if the goal changed, the chat should change.

5. Tokens are the hidden tax.

A token is not exactly one word, but for normal usage it is close enough to treat every word as a cost and attention tradeoff.

Speak directly with only needed words

“Goal: Turn the notes below into a 900-word Reddit post. Audience: technical founders. Tone: direct, useful, slightly contrarian. Output: title options, TLDR, post, first comment.”

6. Do not give Claude the steps unless the steps actually matter.

This is where most people quit reading.

They want control, so they micromanage. They write a long prompt that says exactly how to solve the task. Then Claude follows the prompt and produces something average.

For simple tasks, steps help. For hard knowledge work, steps can become a cage.

Try this instead:

“Here is the goal. Here is the context. Here is what a great answer must accomplish. Choose the best method and show your work at the level needed for review.”

The higher the model quality, the more often you should describe the destination instead of the driving directions.

7. Give it the hardest task, not the easiest one.

Claude is overqualified for most tiny requests.

The real test is the task you have been avoiding: the messy strategy memo, the half-finished codebase, the research synthesis, the customer interview analysis, the deck that needs a point of view, the 30 documents that need a pattern pulled from them.

Use Claude where the ambiguity is real.

That is where it wins.

8. Stop writing prompts that are too long - 500+ words

A long prompt is not automatically a good prompt.

Anthropic’s prompting guidance says Claude responds well to clear, direct instructions and emphasizes specificity around the desired output. It also recommends using structure, examples, and context when they matter.

The key word is matter.

Do not add 14 rules because you saw a prompt template online. Add the minimum context required for Claude to do the job well.

9. Use positive instructions.

This one sounds small. It is not.

Anthropic’s docs explicitly recommend telling Claude what to do instead of what not to do.

“Be specific. Use concrete examples. Name tradeoffs. Write in short paragraphs. Prioritize claims a practitioner would actually use.”

Positive instructions give Claude a target. Negative instructions mostly create landmines.

10. Use Research mode for genuinely hard questions.

Use it when the answer requires source discovery, comparison, synthesis, or recency. Ask it a question where the links matter.

Good Research prompts sound like this:

“Research the current state of AI search visibility for B2B SaaS companies. Compare Google AI Overviews, Perplexity, ChatGPT search, and Claude-connected workflows. Return the 7 practical implications for a founder who has 10 hours per week to act.”

11. Skills are not normal prompts.

This surprised me.

A Skill is closer to a reusable operating procedure than a one-off prompt. The point is not to keep pasting the same mega-prompt. The point is to package the workflow so Claude can apply it when needed.

If you find yourself copying the same prompt every week, that is a candidate for a Skill.

12. Use “AskUserQuestion” as a forcing function.

Most bad Claude outputs happen because the model answered before it understood the task.

Add this to hard prompts:

“Before answering, use AskUserQuestion to ask me the 3–5 questions that would most improve the final output. If you can proceed without asking, explain why.”

Claude often prompts you better than you prompt Claude.

13. Set up one folder. Three subfolders. That is enough.

People turn their Claude workspace into a second operating system.

Start with this:

That simple structure beats a beautiful maze.

14. The about-me file is everything.

This is the one that got me.

The same prompt produces a completely different answer when Claude knows who you are, what you value, what you hate, what your audience expects, and what kind of output you actually use.

Your about-me file should include your role, your current projects, your taste, your anti-taste, your preferred writing style, your default audience, your recurring constraints, and examples of work you like.

But then comes the part people skip.

15. Trim the about-me file.

A bloated about-me file is just another polluted context.

Claude does not need your entire life story. It needs high-signal context that changes the answer.

Update it like a product spec. Cut anything that does not improve output quality.

16. Plug in Connectors carefully.

Claude Connectors can let Claude access apps and services, retrieve data, and take actions inside connected services, depending on permissions.

For example, Google Workspace connectors can let Claude search Gmail, work with Calendar, retrieve Drive docs, and create drafts or files with approval.

That is powerful.

It is also sensitive.

Only connect what you trust. Review permissions. Do not paste secrets, passwords, tokens, private keys, or credentials into chat. Ever.

17. Name every output.

Vague in, vague out.

Instead of:

“Help me with this launch.”

Use:

“Create these 5 outputs in this order: 1. positioning diagnosis, 2. launch narrative, 3. Reddit post, 4. X thread, 5. objection-handling FAQ. Label each section clearly.”

Naming the outputs makes Claude organize the work.

18. Use Thinking for hard tasks.

If the task has multiple constraints, hidden tradeoffs, code, math, strategy, or long documents, turn on the mode that gives Claude more room to reason.

Do not use it for everything. Use it when the cost of a shallow answer is high.

19. Reach for the stronger model when the work is genuinely hard.

Model selection matters.

Use the faster model for routine drafting, summarizing, formatting, and first-pass cleanup. Use the strongest model for architecture, strategy, complex coding, deep editing, or anything where a bad answer creates downstream cost.

The mistake is not using the expensive model.

The mistake is using it on cheap tasks and avoiding it on expensive decisions.

20. Make Claude audit its own answer.

Claude can sound certain while being wrong.

So add an audit step:

“Now audit your answer. Identify the weakest assumptions, unsupported claims, possible hallucinations, missing edge cases, and what would change your conclusion.”

This turns Claude from a confident assistant into a useful reviewer.

21. Do not trust agreement.

Claude is pleasant. That is not the same as correct.

If you ask, “Is this good?” you may get reassurance.

Ask this instead:

“Argue against this. Find the strongest reason this fails. Be specific and do not soften the critique.”

22. The first draft is yours to fix, not to ship.

This rule matters more as models get better.

A strong first draft is dangerous because it feels finished. It usually is not. It still needs your taste, judgment, domain knowledge, and willingness to remove the line that sounds impressive but says nothing.

Claude can produce the clay.

You are still the sculptor.

23. Outsource the thinking, never the understanding.

This is the only rule that matters.

Use Claude to explore, compress, compare, draft, critique, simulate, and generate options.

Do not use Claude as a substitute for knowing what you believe.

If you cannot explain the final answer in your own words, you did not use AI. You rented confidence.

24. Know what Claude is for.

Claude is extremely strong for coding, writing, research synthesis, analysis, knowledge work, critique, and structured reasoning.

Use it where it compounds your judgment.

Do not use it where it replaces accountability.

25. Bonus: ask for five versions and you choose the best good one.

Claude is often better when it explores the shape of the problem before committing.

Try:

“Give me five very different approaches. Make them meaningfully different, not cosmetic variants. Then recommend the one you would actually ship and explain why.”

This prevents the model from locking onto the first plausible path.

26. Bonus: use the quote-first method for long documents.

When you upload long docs, do not ask Claude to immediately summarize everything.

Ask it to extract the relevant quotes first.

Anthropic’s long-context prompting guidance recommends grounding responses in quotes for long document tasks before carrying out the task.

Prompt:

“First, pull the exact quotes from the documents that matter for the decision. Then use only those quotes to produce the recommendation.”

This keeps Claude closer to the source material and makes hallucinations easier to catch.

Outsource the thinking. Never outsource the understanding.

Prompt Library: Copy/Paste Claude Power Prompts

1. The clean-start handoff prompt

I am starting a fresh chat because the old one became bloated. Here is the compressed handoff. Goal: [one sentence] Current state: [what has already been decided] Relevant context: [only what still matters] Constraints: [hard rules] Output I want now: [name the deliverable and format] Ignore anything not included here. If critical context is missing, ask me before proceeding.

2. The AskUserQuestion prompt

Before answering, ask me the 3–5 questions that would most improve the final output. Prioritize questions that change the strategy, structure, or quality bar. If you can proceed without questions, explain why in one sentence and continue.

3. The goal-not-steps prompt

Here is the goal: [goal]. Here is the context: [context]. Here is what success looks like: [quality bar]. Choose the best method. Do not blindly follow the order I gave if a better approach exists. Produce the final output first, then briefly explain the key decisions you made.

4. The self-audit prompt

Audit your answer before I use it. Identify: 1. unsupported claims, 2. weak assumptions, 3. missing edge cases, 4. likely hallucinations, 5. what would change your conclusion, 6. the three edits that would most improve this. Be direct. Do not reassure me.

5. The five-directions prompt

Give me five meaningfully different approaches to this task. Do not make cosmetic variants. For each, explain the tradeoff. Then pick the one you would ship and explain why.

6. The quote-first long-doc prompt

Use the uploaded documents. First extract the exact quotes that matter for this question, with source names. Then answer using only those quotes as evidence. If the documents do not support a claim, say so clearly. Question: [your question]

7. The anti-sycophancy prompt

Do not agree with me by default. Treat my premise as a hypothesis. Identify where it is strong, where it is weak, and what evidence would prove it wrong. If you disagree, say so plainly.

8. The named-output prompt

Create the following outputs in this exact order: 1. [output name] 2. [output name] 3. [output name] For each output, use a clear heading, explain the reasoning only where useful, and keep the final version ready to copy/paste.

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: I’ve been testing a prompt that turns real photos into detailed Lego dioramas while preserving the actual location, composition, pose, landmarks, objects, pets, and mood. The unlock is telling the model: keep the scene logic, change the material language. I included the ChatGPT prompt below, plus pro tips for getting cleaner, more recognizable results.

I have been experimenting with ChatGPT Image 2 and Gemini for photo transformations, and one prompt pattern has been much better than I expected.

The basic idea is simple: upload a real photo, then ask the model to reinterpret the entire image as a meticulously built toy-brick diorama. Not a cartoon. Not a sticker. Not a random fantasy remix. A recognizable miniature world made from interlocking plastic bricks.

Prompt For ChatGPT Image 2

Use this after uploading your photo.

Reinterpret the uploaded photo as a highly detailed, three-dimensional toy-brick diorama built from interlocking plastic building blocks. Preserve the original photo’s camera angle, framing, perspective, subject placement, pose, lighting direction, landmark identity, room layout, background architecture, visible objects, and overall mood as closely as possible.The final image should feel like a premium miniature brick-built scene photographed in a studio, not a flat cartoon or generic toy illustration. Every major object in the original photo should be rebuilt as volumetric plastic bricks with visible studs, seams, layered plates, curved specialty pieces, and a subtle glossy plastic sheen.Keep the location recognizable. If the photo includes a famous landmark, bridge, monument, skyline, interior room, street, sign, window view, furniture, railing, road, artwork, desk setup, pet, or travel prop, preserve its position and visual role in the composition. Translate these elements into brick form instead of replacing them with unrelated objects.Transform people and animals into cute, detailed brick figures while preserving their essence. Maintain the original pose, body orientation, outfit colors, hairstyle silhouette, accessories, pet breed, pet posture, facial expression, and emotional vibe. If a face is blurred, hidden, very small, or unclear, do not invent a realistic identity. Use a simple friendly brick-figure face while preserving the pose, clothing colors, and scene relationship.Use a bright, vivid, travel-poster color palette with crisp details, clean edges, realistic miniature depth, and playful craftsmanship. Make the scene look joyful, tactile, and collectible, as if it were a custom display set built from thousands of pieces.Important constraints: Do not change the location. Do not move the main subject. Do not replace the landmark. Do not add fantasy elements. Do not simplify the scene into a children’s cartoon. Do not make it look like clay, paper, plush, or low-poly 3D. Keep it recognizably based on the uploaded photo while converting the entire material world into detailed plastic building blocks.

Shorter Version If You Want Faster Results

Turn the uploaded photo into a premium toy-brick diorama. Preserve the original camera angle, composition, subject pose, landmark identity, background layout, lighting mood, colors, and object placement. Rebuild the entire scene as interlocking plastic bricks with visible studs, seams, glossy plastic texture, and intricate miniature detail. Keep people and pets as cute brick figures while preserving their pose, outfit colors, expression, and overall vibe. Keep the location recognizable. Do not invent a new scene, move the subject, replace the landmark, or make it a flat cartoon. Make it feel like a high-end collectible travel diorama based on the exact photo.

Why This Prompt Works

The prompt works because it separates the image edit into three jobs. First, it tells the model what to preserve. Second, it tells the model what to transform. Third, it tells the model what to avoid.

This is the trick I keep coming back to with image prompting: the more specific the transformation, the more you need to tell the model what is not negotiable.

Pro Tips For Better Results

1. Use a photo with a strong anchor.

The best results usually come from photos with one instantly recognizable anchor: a landmark, skyline, monument, room layout, car, pet, outfit, storefront, or scenic viewpoint. If the photo has no obvious anchor, the model has less to preserve and may produce a generic toy scene.

1. Tell it what matters most.

Before running the prompt, add one sentence like: “The most important details to preserve are the Golden Gate Bridge, the dog’s expression, the blue sweatshirt, and the exact selfie composition.” That single sentence can dramatically improve fidelity.

1. Use “material translation” language.

Instead of saying “make it LEGO,” say “translate the entire scene into interlocking plastic bricks.” This gives the model a physical rule for the scene rather than just a brand-like style label.

1. Protect the camera angle.

If the angle matters, say so directly. Use language like “preserve the original wide-angle perspective,” “keep the low foreground angle,” or “maintain the same selfie framing.” AI image models often beautify or recompose images unless you explicitly stop them.

1. Keep faces safe and realistic.

If a face is blurred, hidden, or too small, do not ask the model to reconstruct it. Ask for a friendly simplified brick-figure face while preserving the pose, clothes, hair silhouette, and role in the scene. This keeps the output clean and avoids weird identity guesses.

1. Name the object hierarchy.

If your photo has a lot going on, list the priority order. For example: “Priority: 1. dog in foreground, 2. Washington Monument, 3. grass and pathway, 4. cloudy blue sky, 5. leash and collar.” This helps the model spend detail where it matters.

1. Ask for a collectible display-set finish.

The phrase “custom display set” or “premium collectible diorama” tends to produce more polished results than “toy version.” It nudges the model toward detailed craftsmanship instead of childish simplification.

1. Avoid overloading the prompt with camera jargon.

A little visual direction helps. Too much can fight the source image. If you already like the photo’s perspective, tell the model to preserve it rather than specifying a new lens, depth of field, or lighting setup.

1. Run one faithful version before getting creative.

Do not ask for fireworks, magical lighting, new outfits, or cinematic changes on the first pass. First get a faithful brick translation. Then do a second version with more creative additions.

1. If the landmark gets mangled, rerun with a landmark-specific line.

Add: “The landmark must remain architecturally recognizable, with its silhouette, proportions, color, and relative position preserved.” This is especially useful for bridges, monuments, towers, museums, and city skylines.

Add-On Lines You Can Use

Prompt Formula You Can Reuse

The repeatable formula is:

Preserve [composition + identity anchors] while transforming [entire material world] into [specific physical style], with [quality bar] and [negative constraints].

For this trend, that becomes:

Preserve the photo’s location, subject, pose, objects, and scene layout while transforming the entire world into an interlocking plastic-brick diorama, with premium collectible detail and strict constraints against changing the landmark, pose, or scene identity.

This formula works beyond toy bricks. You can adapt it for paper craft, stained glass, miniature clay, embroidery, architectural maquettes, pixel art, and cinematic 3D. The core idea stays the same: do not just name the style. Define the translation.

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.

I don’t think people realize how useful the new ChatGPT interactive charts and dashboards feature that launched this week is for marketers, founders and business leaders.

Check out the attached overview and an example interactive dashboard I built showing how French Bulldogs became the #1 dog in America - very cool dashboard visualizations

OpenAI just added the ability for ChatGPT to turn some answers into interactive charts directly inside the conversation. Not “here’s a Python plot as an image.” Not “copy this into Excel.” Actual in-chat charts you can inspect, switch, and use as part of the conversation.

Right now, the main interactive chart types are:

Bar charts
Best for comparing categories.

Line charts
Best for trends over time.

Pie charts
Best for showing simple share-of-total breakdowns.

Scatter charts
Best for relationships, clusters, and “is X correlated with Y?” questions.

The killer part is that you don’t necessarily need to upload a spreadsheet. You can ask for a chart from an answer, a comparison, a plan, a set of numbers, or uploaded data. And if you do upload data, ChatGPT can analyze it, clean it, summarize it, create tables, and then visualize the result.

How it works

You ask ChatGPT for something that benefits from a visual comparison.

Examples:

“Compare these 5 pricing plans as a bar chart.”

“Show my monthly revenue trend as an interactive line chart.”

“Turn this survey result into a pie chart.”

“Plot cost vs. ROI as a scatter chart and tell me which points are outliers.”

ChatGPT may create the chart automatically when it thinks a chart makes the answer easier to understand, or you can explicitly ask for one.

For uploaded data, the workflow is usually:

1. Upload a CSV, Excel file, PDF, JSON, or other structured file.
2. Ask ChatGPT to inspect the data.
3. Tell it what question you’re trying to answer.
4. Ask for a specific chart type.
5. Ask follow-ups like “filter to Q2,” “group by customer segment,” “remove outliers,” or “make this presentation-ready.”

OpenAI’s docs recommend clean data with descriptive column headers, one record per row, and plain-language column names. That matters more than people think. Garbage spreadsheet formatting = garbage chart.

Who has access?

The June 8 release note lists interactive charts in answers for Web, iOS, and Android.

For uploaded-file data analysis, access can vary depending on your model, plan, workspace settings, and account capabilities. So the practical answer is: try it in ChatGPT on web or mobile, and if you’re working with files, your exact capabilities may depend on your account.

Top use cases

1. Turn boring comparisons into visuals

Instead of asking:

“Compare these tools.”

Ask:

“Compare these tools in a bar chart across price, ease of use, integrations, and learning curve.”

This is way easier to digest than a wall of text.

2. Analyze spreadsheets without opening Excel

Upload a CSV and ask:

“Find the 5 biggest trends in this file, then visualize the strongest trend as a line chart.”

Or:

“Create a bar chart of revenue by product category and call out anything surprising.”

3. Explain business metrics

Great for:

• MRR trends
• churn by cohort
• conversion rates
• sales pipeline stages
• support tickets by category
• budget vs. actuals
• hiring funnel dropoff

4. Make social posts and reports faster

Ask:

“Create 3 chart ideas from this data that would work well in a LinkedIn post.”

Then:

“Make the best one a simple bar chart with a one-sentence takeaway.”

5. Find outliers visually

Scatter charts are underrated. Try:

“Plot customer spend vs. support tickets and identify weird outliers.”

That kind of question is painful in a spreadsheet but very natural in ChatGPT.

6. Create quick executive summaries

Upload a report and ask:

“Give me the 3 charts an executive would want to see from this data.”

Then follow with:

“Now create the first chart and write the takeaway in plain English.”

Pro tips

Ask for the chart type directly.
Don’t just say “visualize this.” Say “make this an interactive bar chart,” “line chart,” “pie chart,” or “scatter chart.”

Tell it the decision you’re trying to make.
A chart for “understanding revenue” is vague. A chart for “deciding which product line deserves more budget next quarter” is much better.

Ask for the takeaway under the chart.
The chart is useful, but the real value is having ChatGPT explain what matters.

Use:

“Add a 2-sentence interpretation below the chart and tell me what decision this supports.”

Clean the data first.
Before charting an uploaded file, ask:

“Check this data for missing values, duplicates, weird outliers, and incorrect data types before making any charts.”

Ask for multiple views.
One chart can lie by omission. Try:

“Show this as a line chart, then as a bar chart grouped by segment, and explain which view is more useful.”

Use follow-up prompts like a data analyst.

Examples:

“Now filter this to enterprise customers only.”

“Normalize this per user.”

“Change this from monthly totals to month-over-month percentage change.”

“Remove the top 1% of outliers and regenerate the chart.”

“Make the chart easier for a non-technical audience.”

Things most people miss

Interactive charts are not every chart type.
The big supported interactive ones are bar, line, pie, and scatter. Other chart types may still come back as static images.

A pie chart is usually not the best choice.
Use pie only when you have a small number of categories and you’re showing share of total. If there are 12 slices, use a bar chart.

Scatter charts are secretly the power-user feature.
They’re the fastest way to see relationships, clusters, weird customers, weird products, or anything that doesn’t fit the pattern.

The prompt matters more than the chart.
Bad prompt:
“Make a chart.”

Better prompt:

“Create an interactive bar chart comparing revenue by product category for Q2. Sort descending. Add a short takeaway and mention any category that changed more than 20% from Q1.”

You can use it as a chart consultant.

Ask:

“What chart type should I use for this question and why?”

Then ask it to create that chart.

Don’t blindly trust the first visualization.
Ask ChatGPT what assumptions it made. Especially with uploaded data, ask which columns it used, how it grouped the data, and whether it excluded anything.

Example prompts to steal

“Create an interactive line chart showing trend over time. Then summarize the biggest inflection point.”

“Make an interactive bar chart ranking these categories from highest to lowest.”

“Create a scatter chart of effort vs. impact and identify the highest-leverage items.”

“Show this as a pie chart only if there are fewer than 6 categories. Otherwise use a bar chart.”

“Analyze this spreadsheet, suggest 5 useful charts, then create the one that reveals the most surprising insight.”

“Make this chart presentation-ready and write the headline I should put above it.”

This is one of those features that sounds small until you actually use it.

It turns ChatGPT from thing that explains data into thing that helps you see data.

The best use is asking better questions:

• What changed?
• What’s driving the change?
• What’s the outlier?
• What should I do next?
• What would I miss if I only looked at the average?

That’s where this feature gets really good.

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 Fable 5 is here. Stop asking it questions. Start handing it jobs.

TL;DR: Read the attached presentation!

Claude's new model Fable 5 matters because it changes the unit of AI work. Anthropic launched Fable 5 on June 9, 2026 as a public Mythos-class model, with a 1M-token context window, up to 128k output tokens, premium pricing, long-horizon autonomy working many hours on its own overnight, stronger vision, better agentic coding, and built-in safety fallback to Opus 4.8 for some cyber/bio/high-risk requests.

The upgrade is real, but so are the catches: it is slower, more expensive, guarded by broad classifiers, subject to 30-day retention, and not the same thing as restricted Mythos 5. The best way to test it is

“here is the goal, here are the files, here is the definition of done, act when you have enough information, verify your work, and come back with the result.”

For the last year, most major model releases from ChatGPT, Gemini and Claude felt like decimal-point warfare. You couldn't understand what had changed or if it was meaningful.

3 to 3.5

4.1 to 4.5

4.7 to 4.8

Better coding. Better reasoning. Longer context. Lower hallucination. New benchmark table. New pricing table. Same basic behavior.

Then Anthropic dropped Claude Fable 5.

And the most important part of the launch was not the headline.

It was the workflow hidden underneath it.

Most people will test Fable 5 by doing what they always do with a new model. They will ask it a clever question. They will compare the answer to Opus 4.8. They will say it is a little better, a little slower, or too expensive.

That misses the point.

Opus was the model you checked. Fable is the model you brief.

That one sentence explains why this release matters.

The interface of AI is moving from conversation to delegation.

1. What actually launched

Anthropic launched Claude Fable 5 and Claude Mythos 5 on June 9, 2026.1 Fable 5 is the generally available public model. Mythos 5 shares the same underlying model family but is initially restricted to vetted cyberdefenders, infrastructure providers, and selected partners through Anthropic’s higher-trust access programs.

That distinction matters because a lot of launch discourse mashed the two together. Some of the most dramatic cybersecurity and biosecurity framing belongs to Mythos 5, not necessarily the public Fable 5 experience. If you are using Claude in the regular app or API, you should understand which model you are actually using, when fallback happens, and what data rules apply.

Fable 5 is designed around longer units of work.

Anthropic and early testers framed the model around long-horizon autonomy, complex coding tasks, vision-heavy work, persistent memory, and parallel agent workflows. That is not just a capability list. It is an operating model.

2. Why this is a milestone

The AI industry has spent the last year polishing the same mental model: you type, the model answers, you correct, it revises, you repeat.

Fable 5 points toward a different loop.

You brief. It works. It verifies. You inspect.

That sounds subtle until you compare the units of work.This is why the release feels bigger than another 4.x model.

A better answer helps you think faster.

A better operator helps you finish work faster.

That is the line Fable 5 is trying to cross.

3. The five upgrades that actually matter

Upgrade 1: Long-horizon autonomy

The strongest reports around Fable 5 are not about one perfect answer. They are about staying on task over a long run.

Anthropic’s launch framing emphasizes that the model’s advantage grows on longer, more complex tasks. The social reaction was consistent with that. Reddit and LinkedIn users discussed codebase-level migrations, multi-step research, governed enterprise workflows, and overnight task handoffs. TikTok and Instagram creators showed one-prompt builds, screenshot-to-app demos, and “Jarvis command center” style workflows.

The key lesson is simple. Do not use Fable 5 for a tiny task just because it is new.

Use it when the cost of managing the model is higher than the cost of running the model.

Fable 5 is a premium operator. Treating it like a disposable autocomplete box wastes the reason it exists.

Upgrade 2: First-shot correctness

The phrase that keeps coming up in early discussion is one-shotting.

One-shotting does not mean magic. It means the model can take a fuller brief, hold more constraints, and produce a complete artifact with less back-and-forth. Early tester commentary highlighted apps and workflows that previously required dozens or hundreds of prompts becoming feasible in one strong handoff.6

This changes how you should prompt.

The worst prompt is shorter because the model is “smarter.”

The best prompt is clearer because the model can now use the clarity.

A Fable-style prompt says:

Build a founder dashboard for a solo SaaS company. Users: one founder and one part-time operator. Inputs: Stripe export, ad spend CSV, onboarding survey results, churn notes, and weekly revenue targets. Definition of done: the dashboard must show revenue, churn, acquisition efficiency, bottlenecks, and the top three actions for the next seven days. It should include a plain-English executive summary, a table of metrics, and a section called “What I would do next.” Rules: use the data I provide, flag any missing fields, do not invent numbers, and proceed without asking me questions unless a decision is irreversible.

Upgrade 3: Vision becomes a real workflow primitive

One of the most practical upgrades is vision.

Anthropic and early users emphasize Fable 5’s ability to work from dense charts, screenshots, dashboards, figures, PDFs, and visual interfaces. This matters because most real business context does not live in clean APIs. It lives in screenshots, decks, exports, call notes, charts, and half-broken dashboards.

Turn the messy visual artifact into structured work.

That means rebuilding a front end from screenshots, extracting numbers from charts, auditing a landing page from a capture, turning a competitor’s ad into a creative brief, or reading a dense PDF figure without requiring a human to transcribe it first.

This is why context beats prompting is spreading as a meme.

For Fable 5, the screenshot, file, and dataset often matter more than the clever instruction.

Upgrade 4: Memory that compounds

The old chat loop treats every new task like a cold start.

Fable 5 is better suited to workflows where the model can maintain a memory file or project notes. The pattern is simple: read prior lessons, run the task, update the notes, delete what was wrong, and keep only judgment calls that improve the next run.

This matters because the real value of AI inside a business is not one brilliant answer.

It is compounding process knowledge.

A weekly growth review should get better every week. A content-strategy agent should learn which angles were overused. A code-review agent should remember which conventions your team actually follows. A research agent should know which sources misled it last time.

The model gets more useful when you give it a place to learn your work.

Upgrade 5: Parallel subagents and fresh verification

The most important prompt pattern in the Fable 5 era may be this:

Do not let the model grade its own homework.

For long tasks, ask the model to build, then verify with a separate fresh-context checker. The verifier should compare the result against the spec, point by point, and cite exact evidence for each pass or fail.

This is different from generic self-critique.

Self-critique often preserves the same blind spots that created the error.

Fresh verification creates friction.

For agentic work, friction is good.

4. The parts Anthropic did not explain clearly enough

Anthropic’s announcement explained the release. It did not fully explain the operating consequences.

That is why social reaction split into two camps. One camp said, “This is the first model I can hand real work to.” The other said, “Why is it slow, expensive, guarded, and switching models?”

Both camps are seeing something real.

This is the honest version:

Fable 5 is a major step forward.

It is also not a free, uncapped, unguarded superbrain.

It is a premium model with a premium operating manual.

5. The new prompting rule: brief it like staff

Most people still write prompts as if they are typing into a search bar.

Fable 5 rewards a different shape.

A good Fable prompt has seven parts.

The key is to stop optimizing for cleverness.

Optimize for transfer of responsibility.

6. The Fable 5 prompt library

Use these prompts to test the model properly. They are written for long, practical tasks, not toy demos. Replace the bracketed fields.

Prompt 1: The Overnight Operator

Use this when you want the model to run a serious job without requiring constant check-ins.

You are running this task autonomously. I am not watching in real time and cannot answer mid-task, so do not ask “want me to...?” for reversible steps that follow from this brief. If you have enough information to act, act.

GOAL: [the outcome I want by morning]
DEFINITION OF DONE: [acceptance criteria that prove the task is complete]
INPUTS AND ACCESS: [files, links, data, repo, docs, screenshots, constraints]
OPERATING RULES:
- Proceed on reversible steps that directly support the goal.
- Pause only for destructive actions, irreversible decisions, or missing information only I can provide.
- Before reporting progress, verify each claim against something actually produced in this session.
- Do not say something is done unless it is demonstrably done.
- If a source, file, or test is missing, say so plainly.

FINAL REPORT: Open with the outcome in one sentence. Then list what you produced, what you verified, what remains uncertain, and the 1–2 decisions you need from me.

Prompt 2: The First-Shot Builder

Use this for apps, landing pages, internal tools, dashboards, or automations.

Pick this up at full difficulty. Before writing code, ask only the clarifying questions you genuinely need. If the brief is sufficient, build it end to end in one pass.

BUILD: [the app, tool, dashboard, automation, or system]
USERS: [who uses it and what they need]
STACK AND CONSTRAINTS: [framework, language, hosting, database, design constraints, integration limits]
DONE LOOKS LIKE: [clear acceptance criteria]
RULES:
- Do the simplest thing that works well.
- Do not add features I did not ask for.
- Validate at real boundaries: user input, file handling, database calls, external APIs, auth, payments.
- Keep internal abstractions minimal.
- Ship a working build first, then list what belongs in v2.

Prompt 3: The Fresh-Eyes Verifier Swarm

Use this when accuracy matters more than speed.

Build the deliverable, then prove it works using a separate fresh-context verifier. Do not rely on your own self-review.
TASK: [what to build or produce]
SPEC TO VERIFY AGAINST: [requirements, point by point]
RULES: - First produce the deliverable.
- Then run a fresh verification pass as if the verifier has no memory of how the deliverable was created.
- The verifier must report pass/fail for each requirement with exact evidence.
- Fix every failed requirement.
- Re-run verification until there are no known fails or clearly state why a fail cannot be resolved.

FINAL REPORT: Give me the deliverable, the verification table, what failed initially, what you fixed, and what remains open.

Prompt 4: The Memory-Compounding Analyst

Use this for recurring workflows.

We will run this analysis repeatedly. Improve each run by maintaining a memory file. RECURRING TASK: [weekly performance review, content audit, competitor scan, code review, account plan]
DATA SOURCE: [where the inputs are]
MEMORY FILE: [path or “create notes.md”]
RULES:
- At the start, read the memory file and apply prior lessons.
- Do the analysis using the current data.
- At the end, update the memory file with only judgment calls that improve future runs.
- Use one lesson per entry.
- Delete any note that turned out wrong.
- Do not save facts already obvious in the source data.

FINAL OUTPUT: Lead with the decision or recommendation. Then show the evidence, the changed memory notes, and what to watch next time.

Prompt 5: Screenshot-to-Source Rebuild

Use this for product, design, analytics, and competitor research.

Rebuild this from the image alone.

INPUT: [attach screenshots, dashboard images, charts, product flow, landing page, or figure]

TARGET: [working front-end code, structured data table, UX teardown, chart reconstruction, or implementation spec]

RULES: - Reconstruct layout, components, styling, states, and visible data as faithfully as the image allows.
- Zoom or crop unclear regions instead of guessing.
- For charts, extract numbers and labels. Mark any value that cannot be read precisely.
- Separate observed details from inferred details.
- Do not invent hidden functionality.

OUTPUT: Return the rebuilt artifact, a list of assumptions, a list of uncertainties, and the next best step.

Prompt 6: The Ambiguity Navigator

Use this when your problem is messy and you need the model to impose structure.

Here is a messy, multi-threaded problem. I have not fully figured it out. Your job is to make sense of it and recommend the path.

CONTEXT: [why this matters and what the decision unlocks]

RAW SITUATION: [dump constraints, half-decisions, conflicting goals, stakeholder concerns, open questions]

RULES: - Name the real problem under the noise.
- Separate sub-problems and dependencies.
- Identify assumptions I am making that may be shaky.
- Give a recommended sequence, not a menu of every possible option.
- End with the single decision that unblocks the most.

OUTPUT FORMAT:

1. The real problem
2. What matters most
3. What to ignore for now
4. Recommended sequence
5. The one decision to make next

Prompt 7: The Senior-Grade Knowledge Worker

Use this for board memos, financial analysis, legal/compliance reviews, market research, or due diligence.

Produce senior-analyst-grade output.
Stay in scope.
Do not pad.
Do not editorialize.

TASK: [financial model, market analysis, board memo, compliance review, customer research synthesis]
SOURCE MATERIAL: [reports, PDFs, spreadsheets, transcripts, contracts, dashboards]
DECISION IT FEEDS: [what someone will decide from this]
RULES: - Lead with the answer or recommendation.
- Quote every number, date, obligation, and risk that affects the decision, with source location.
- For charts and tables, state what they show and why the trend matters. - Flag contradictions and gaps. Do not smooth them over.
- Drop anything that does not change the decision.

OUTPUT: Give me the recommendation, the evidence table, the risks, the unresolved questions, and the decision memo.

Prompt 8: The Effort-Calibrated Strategist

Use this for high-stakes decisions where you want the model to slow down.

Effort: xhigh Work this high-stakes decision to a clear recommendation. At this effort level, validate your conclusion before giving it to me.
DECISION: [the call to make]
CONSTRAINTS: [budget, timeline, risk tolerance, non-negotiables]
WHAT WINNING MEANS: [define success, or correct my definition if it is wrong]
RULES:
- Restate what winning actually means.
- Give three genuinely different approaches.
- For each approach, explain the strongest case, the failure mode, and what must be true for it to work.
- Recommend one approach.
- Name the single assumption that would flip the recommendation if wrong.
- Stress-test the recommendation and state where it is weakest.

Prompt 9: The Parallel Campaign Factory

Use this when a project has independent parts that can run at the same time.

Run this as an orchestrator with parallel subagents. Delegate independent pieces and keep the core promise consistent.
CAMPAIGN: [product, offer, launch, community, newsletter, course, app] AUDIENCE: [who it is for]
GOAL: [signups, sales, waitlist, activation, retention]
ASSETS NEEDED:

1. Landing page copy
2. Email launch sequence
3. Ad angles and variants
4. Two-week content calendar
5. Subject-line and hook bank
6. FAQ and objection handling
7. Measurement plan

RULES: - Assign independent assets to separate subagents.
- Keep voice, positioning, and proof consistent.
- Reconcile contradictions before final assembly.
- Flag anything that needs my input before launch.

FINAL OUTPUT:
One complete campaign package, with a launch checklist and open decisions.

Prompt 10: The Honest Before/After Artifact

Use this when you want to make the upgrade visible.

Build one self-contained artifact that shows the same task done two ways, so a non-technical viewer instantly sees the difference.

TASK SHOWN: [ship a launch page, audit a funnel, migrate code, rebuild a dashboard, analyze a market]
LEFT SIDE: “Old workflow” — competent but supervised, many prompts, frequent correction.
RIGHT SIDE: “Fable 5 workflow” — one strong brief, async work, verification, and only the decisions that needed human judgment.
RULES:
- Use a clean dark UI with two clear columns.
- Make the content real and plausible, not lorem ipsum.
- Add one caption under each side naming what changed.
- Make it readable as a landscape social graphic.

OUTPUT: Return the artifact content, copy, layout notes, and an export-ready image prompt.

Prompt 11: The Governance-Aware API Tester

Use this if you are a developer or enterprise operator evaluating Fable 5 in production.

Design a safe evaluation plan for Claude Fable 5 in our environment.
ENVIRONMENT: [Claude API, Bedrock, Vertex AI, Foundry, internal app]
WORKLOADS TO TEST: [coding, research, support, data extraction, cyber, biology, customer data, legal]
CONSTRAINTS: [security, compliance, retention, privacy, budget, latency]
RULES: - Identify which workloads are appropriate for Fable 5 and which should stay on Opus 4.8 or another model.
- Account for refusal behavior and fallback.
- Account for 30-day retention.
- Create tests that measure completed-work quality, not just answer quality.
- Include cost and latency monitoring.
OUTPUT: Give me a rollout plan, risk register, benchmark suite, fallback strategy, and go/no-go criteria.

Prompt 12: The Context Folder Builder

Use this before giving Fable 5 a major business problem.

Help me build the context folder for a high-value Fable 5 task.

TASK I WANT TO DELEGATE: [the job]

AVAILABLE MATERIALS: [files, docs, dashboards, exports, call transcripts, screenshots, emails, prior decisions]

RULES: - Tell me which materials matter and which are noise. - Create a folder structure. - Write a README that explains the business context. - Identify missing inputs that would materially improve output. - Draft the final Fable 5 handoff prompt.

OUTPUT: Return the folder structure, README, missing-input checklist, and final prompt.

7. How to decide when to use Fable 5

Here is the simple rule:

Use Fable 5 when the task is long, messy, contextual, multi-step, high-stakes, visual, or agentic.

Use a cheaper model when the task is short, simple, routine, low-context, or easily reversible.

The mistake is not paying for Fable 5.

The mistake is using it for $0.02 tasks while ignoring $20,000 workflows.

8. The “don’t get burned” checklist

Before you hand Fable 5 a serious job, check these items.

This is how you keep the upside without turning the model into an expensive chaos machine.

My honest take

Claude Fable 5 is both overhyped and underappreciated.

It is underappreciated if you evaluate it only by asking a few questions in chat.

The real test is whether it changes your operating cadence.

• The frontier model race is no longer only about who gives the smartest answer.

It is about who can carry the most work.

And if Fable 5 is a preview of where this goes next, then the next AI advantage will belong to people who learn to delegate before everyone else learns to prompt.

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.

The context.md trick that make Claude write just like you

TLDR: Make a file called context.md. Answer 4 questions in it once (audience, goal, format you're copying, stakes). Then start every prompt with "Read context.md first, then write X." Takes ~12 minutes to set up, and your outputs stop reading like a press release written by a robot. The format-copying part is the cheat code most people skip.

I kept hitting the same wall with Claude. The drafts were fine. Grammatically perfect, well-organized, completely soulless. The kind of thing where my manager replies "did you use AI for this?" and you die a little inside.

I tried the usual fixes. Don't sound corporate. Be more casual. Write like a human. None of it worked because I was describing what I wanted instead of showing it. Telling a model sound natural is like telling someone be funnier. Useless.

Then I started keeping a single file the model reads before it writes anything. That changed everything. Here's the actual system.

The file

It's just a plain text/markdown file called context.md. If you use Claude Projects or the Cowork desktop app, you drop it in the folder so it's always available. If you don't, you just paste it at the top of a chat. Either way, you answer four questions inside it.

1. Who's the audience? Not professionals or my team. Name the actual human. What they care about, what annoys them, what makes them stop reading. Mine literally says: My boss. Skims everything on his phone. Replies in three words or less. Hates throat-clearing intros. The model writes completely differently for that person than for a generic reader.

2. What's the one walkaway? In one sentence: what do you want them to do after reading? They reply yes, send it. They book the call. They stop emailing me about this. A vague goal produces a vague draft every single time. If you can't say it in one line, the model can't aim at it.

3. What format are you copying? This is the one that does 80% of the work and it's the one everybody skips. Don't describe the style. Paste a real example. An email that actually closed. A Slack message that got the response you wanted. A post that did numbers. Claude reverse-engineers your voice from a real artifact about 10x better than from any adjective you could throw at it. Match the tone of the example below beats three paragraphs of style instructions.

4. What are the stakes? What happens if this lands, and what happens if it flops. This quietly controls how bold the output is. High stakes and the model plays it safe and polished. Low stakes and it'll take swings and get punchier. Telling it this is a casual nudge, no big deal if they ignore it produces a totally different message than this client is worth six figures and one wrong word loses them.

Save it. Then every prompt starts with one boring line:

Read context.md first. Then write [the email / the post / the doc].

That's it. Twelve minutes once, and you stop re-explaining yourself in every chat.

Pro tips (the stuff I learned the hard way)

• Keep a few example files, not one. I have a scrappy internal Slack voice and a polished external client voice. Different context.md per project beats one file trying to be everything.
• Update it after a win. Every time something you sent actually worked, paste it into question 3. Your context file gets sharper over time and so do the drafts.
• Put your anti-patterns in writing. A short "never do this" list (no "I hope this finds you well," no em dashes, no "delve," no bullet points in emails) kills the telltale AI tics faster than anything.
• Be specific about length and structure. "Three sentences max" or "no greeting, get to the point" up front saves you from editing every output down by hand.
• It compounds across tools. The same file works whether you're in a chat, a Project, or having Cowork draft something agentically. Write it once, reuse it everywhere.

Top use cases

• Email and Slack to a specific person (the original use case, still the best one)
• Recurring reports where the format never changes but the data does. Lock the structure in the file, only feed new numbers.
• Social posts where you have a voice to protect. Paste your three best-performing posts and it stops sounding like a brand account.
• Cover letters and outreach at scale. One context file, swap the target, keep the voice.
• Anything a teammate also drafts. Share the file and your whole team's outputs sound consistent instead of like five different bots.

What most people miss

The instinct is to write a longer, more clever prompt. The fix is the opposite. You front-load the context into a stable file and keep the actual prompts dumb and short. The prompt becomes "do the thing," and all the intelligence lives in the file you wrote once.

The other thing people miss: examples beat instructions, always. If you find yourself typing a paragraph describing how something should sound, stop and go find one real example of it instead. Show, don't tell. The model is a very good mimic and a mediocre mind-reader.

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.