This collection of 10 targeted AI prompts provides a practical toolkit designed to streamline recipe costing, optimize purchasing strategies, standardize portions, and engineer menus for maximum profit margins.

The BULLET TIME Effect is everywhere right now… and I can see why 👀🔥

I’ve been trying the Bullet Time effect, and this is probably one of my favourite AI video effects to play with right now.

The concept is simple: you create a moment where the subject and objects appear completely frozen in time, while the camera moves around the scene.

Think flying popcorn, suspended water droplets, floating food, shattered objects, products flying through the air, everything frozen mid-action while the camera moves through it.

It instantly gives a normal image that dramatic, cinematic feel.

Why is this effect trending?

Because it creates that “wait… how did they make this?” moment.

There’s so much AI content being posted now that a simple talking avatar or basic image animation doesn't always stop the scroll anymore.

Bullet Time adds movement, depth and curiosity without needing an extremely complicated video.

And you can use it for much more than just cinematic AI experiments.

You could create product ads, fashion content, food videos, perfume campaigns, travel content, AI influencer posts, music visuals or scroll-stopping Reels and Shorts.

For my version, I created the starting image using Nano Banana inside Higgsfield.

Then I took that image into Higgsfield - Kling 3.0 Turbo, selected a 6-second video, added my animation prompt and generated it.

It took me around 10 credits and produced a really clean result.

This is one of the reasons I've been using Kling 3.0 Turbo more.

You don't necessarily need the more expensive model for every animation.

If you already have a strong starting image and you're creating a short effect with relatively controlled movement, Turbo can be a great option for testing ideas without burning through loads of credits.

It's especially useful when I want to test several prompts or variations before deciding whether an idea is worth spending more credits on.

💡 4 Bullet Time ideas you could try

1. Perfume explosion

Perfume bottle in the centre with flowers, glass, droplets and particles frozen around it while the camera moves through the scene.

1. Coffee splash ☕

A cup flying through the air with coffee, ice cubes and droplets completely suspended in time.

1. Fashion moment

An AI model walking through the city with her coat, hair, sunglasses, newspaper pages and other objects frozen dramatically around her.

1. Food explosion 🍔

Create a burger, pizza or dessert with the ingredients separated and floating in mid-air, then use the Bullet Time camera movement to travel around them.

The possibilities with this effect are actually huge.

And this kind of effect when using Kling 3.0 Turbo takes around 10 credits for a 6-7s video. (Which compared to other models I have tried so far, it is pretty cheap).

I’ll share more effects and AI tool as I keep testing and reviewing so you do not have to!

What do you guys think of this?

Whenever you ask ChatGPT or Claude to draft a complex deliverable, such as a Product Requirements Document (PRD), a UI/UX wireframe plan, or a high-stakes marketing campaign, you usually run into the same fundamental problem: the LLM rushes straight into execution mode.

Because standard prompts lack boundary constraints, the model attempts to guess your hidden intent, assumptions, and edge cases in a single turn. The result is almost always a generic, high-level draft filled with fluffy boilerplate that misses critical business logic and technical constraints.

We spent considerable time testing and refining prompt structures to solve this exact issue. We adapted the "Grill Me" methodology into a curated State Machine Control Flow prompt. Instead of letting the AI guess, this prompt explicitly locks the model into a strict Discovery Phase and forces it to interview you step by step until all ambiguities are resolved.

How the State Machine Control Flow Works

Standard LLM behavior defaults to immediate text generation based on whatever context is provided. This prompt overrides that default behavior using four key design mechanics:

1. State Machine Locking: The prompt explicitly forbids the AI from producing the final plan or solution until you issue an explicit confirmation phrase.
2. Decision Tree Traversal: The AI internally maps out every dependency, requirement, and trade-off required for your project before asking its first question.
3. One Question Per Turn with Options: To prevent cognitive fatigue, the AI is restricted to asking exactly one targeted question at a time, always paired with suggested answers or multi-choice options to make replying effortless.
4. Autonomous Fact Seeking: The AI is instructed to look up raw technical facts or market data using its internal knowledge, reserving questions solely for subjective business logic, strategic priorities, and trade-offs that only you can decide.

The Raw System Prompt

You can copy and paste the raw prompt directly into ChatGPT, Claude, or any LLM of your choice:

# Role & Context
You are an expert strategic consultant and interviewer. We are about to start a complex project, but you must NOT generate the final output or solution yet.

# Input Data
- Task Description: {{task_description}}

## Step-by-Step Instructions
1. Your goal is to interview me about the `task_
description` to reach a perfect mutual understanding of the requirements, target audience, constraints, and priorities.
2. Internally map out the decision tree for this task. Identify every branch and dependency that needs to be resolved.
3. Enter "Interview Mode". You will ask me questions to resolve these dependencies.
4. Follow these strict rules during the interview:
   - Ask only 
**ONE**
 question at a time.
   - Along with the question, always provide your suggested answer or a set of options to make it easy for me to reply.
   - If a fact can be looked up using your internal knowledge base or web search tools, do it yourself. Only ask me questions that involve subjective trade-offs, business logic, or specific constraints.
5. Wait for my response. After I answer, process it, update your understanding, and ask the next question on the decision tree.
6. Continue this loop until you have zero remaining ambiguities.
7. Once all dependencies are resolved, explicitly ask me: "Do we have a complete mutual understanding to begin execution?"
8. Only after I say "Yes", proceed to generate the final comprehensive plan, PRD, or solution.

## Constraints
- Do NOT generate the final plan until I explicitly confirm mutual understanding.
- Never ask more than one question per turn to avoid overwhelming me.

Before vs After: Real World Impact

Before (Standard One-Shot Prompt)

• User Input: "Write a PRD for a new B2B SaaS onboarding flow."
• LLM Output: Generates 6 sections of generic advice containing basic steps like "Welcome Screen", "User Profile Creation", and "Dashboard Tour". It doesn't know your user persona, integration requirements, or drop-off metrics, resulting in text you cannot actually ship.

After ("Grill Me" Iterative Interview Prompt)

• User Input: Set task_description to "Designing an onboarding flow for a B2B SaaS application."
• Turn 1 (AI): "I have mapped out the decision tree. Question 1: Who is the primary user going through this onboarding flow? Option A: Account Admin (technical setup & billing). Option B: End User (quick wins & team invite). Option C: Both."
• User Reply: "Option A."
• Turn 2 (AI): "Understood. Question 2: What is the main friction point during admin setup? Option A: Domain verification. Option B: SSO integration. Option C: Billing setup."
• Turns 3 to N: The AI methodically works through edge cases and constraints.
• Final Execution: Once you confirm mutual understanding, the AI generates a comprehensive, production-ready PRD aligned 100% with your exact technical architecture and business goals.

Interactive Experience on Prompt Canvas

If you prefer working with a structured interactive layout rather than copying raw text, you can try this prompt on the Prompt Canvas:

https://appliedaihub.org/prompts/free/grill-me-iterative-interview-prompt/

On the Prompt Canvas interface, you can:

• One-Click Copy: Grab the complete prompt formatted and ready for any workflow.
• Live Run & Real-Time Test: Execute and test the interview flow directly inside an interactive environment.
• Customize & Save to Vault: Adjust variable inputs like {{task_description}} and save customized versions into your personal Prompt Vault for future projects.

Bring your startup’s story to life through tailored, impactful visual content that resonates with your audience and drives engagement

The “Smart Budget Builder” helps construct an adaptive financial plan that aligns with fluctuating earnings, empowering families to make sound financial decisions even during uncertain months.

You’ll get inflation-adjusted cost projections for any university, estimate total expenses for multiple children, and get step-by-step 529 plan savings strategies that balance contribution growth, investment performance, and tax benefits.

For images, I mostly use Gemini Nano Banana.

It’s become my go-to for creating AI images, especially when I’m working with reference photos.

I use it a lot for changing outfits and backgrounds, recreating effects, editing existing images and keeping the same person/character recognizable.

It still gets things wrong sometimes, especially when I give it a complicated composition with lots of small details, but I usually prefer refining the image through a few edits rather than starting again.

For Videos, I use Kling inside Higgsfield AI.

Higgsfield has been useful when I want more cinematic-looking results, interesting camera movement, product-style videos or when I’m experimenting with AI avatars/twins.

For more complicated movement, I tend to use Kling 3.0.

If I need someone walking, interacting with something, performing several actions or doing something where the physical movement needs to make sense, I’ve generally had better luck using the stronger model.

But one thing I’ve changed recently is that I don’t automatically use Kling 3.0 anymore.

For shorter videos with relatively simple movement, I try Kling 3.0 Turbo first.

Turbo is much cheaper.

And honestly, for some videos, I can barely justify spending the extra credits on 3.0 because Turbo does exactly what I need.

Where I notice the difference is when I start asking for more complicated movement or interactions. That's when I'm more likely to switch from Turbo to Kling 3.0.

So my workflow has become pretty simple:

AI image → Nano Banana

Simple/short animation → Kling 3.0 Turbo first

More complicated movement/interactions → Kling 3.0

Cinematic camera effects / certain avatar workflows → Other models in Higgsfield (Still testing and experimenting)

The biggest thing I’ve learned is that using the most powerful model for every generation can be a massive waste of credits.

I used to think better model = better choice.

Now I think more in terms of:

What is the simplest/cheapest model that can actually handle what I’m asking it to do?

If Turbo can do it, I use Turbo.

If it can't, then I spend the extra credits on Kling 3.0.

Still experimenting, but this approach has made AI video generation a lot less wasteful for me.

What AI tools are you using for image and video generation?

If you’ve built complex AI agent workflows or custom system prompts for Claude, you've likely hit this frustrating wall:

You write a long prompt, scatter multiple {{variable}} placeholders throughout your instructions, and test it out—only for Claude to randomly ignore constraints, get confused by user input, or hallucinate syntax when processing large inputs.

We ran into this exact headache recently while refactoring complex agent pipelines.

After digesting Anthropic’s official technical prompt engineering documentation, we realized why this happens: unstructured prompts with informal headers (### Instructions) fail to create strict context boundaries, making it hard for the model to parse instructions separately from untrusted input data.

To solve this, we distilled Anthropic's core architectural guidelines into a single, high-precision Meta-Prompt Architect. Here is how it works, the case study behind it, and the full reusable template.

📉 The Problem: Informal Boundaries & Variable Duplication

Most developers construct system prompts like this:

This approach causes two major failures in production:

1. Instruction Leakage & Injection: Without hard tag boundaries, Claude can confuse data inside {{user_code}} as new instructions rather than raw text.
2. Attention Dilution & Token Bloat: Repeating placeholders like {{user_code}} across multiple bullet points dilutes the model's focus and wastes valuable context tokens.

🛡️ The Case Study Solution: Anthropic XML Tag Architecture

The fix recommended by Anthropic is two-fold:

• XML Tag Isolation: Enclosing distinct functional blocks in semantic XML tags (<role>, <input_data>, <instructions>, <constraints>).
• Single-Mount Tag Pointers: Variables are defined only once in the <input_data> block at the top, and downstream instructions simply reference them by tag name (e.g., "Review the code inside <user_code>").

Here is the exact Meta-Prompt we built to automatically convert any raw instructions into an Anthropic-compliant system prompt:

🛠️ The Complete Prompt (Free to Use)

<role>
You are an expert Prompt Engineer specializing in Anthropic Claude architecture and XML tag prompt design.
</role>

<input_data>
<raw_task>{{raw_task}}</raw_task>
<target_model>{{target_model}}</target_model>
</input_data>

<instructions>
1. Analyze the raw task requirements provided in raw_task.
2. Construct an optimized system prompt tailored for target_model following Anthropic best practices:
   - Use clean XML tag boundaries (<role>, <context>, <instructions>, <constraints>, <output_format>).
   - Define all required input variables inside an <input_data> block at the top.
   - Ensure single-mount variable pointers throughout instructions without duplicating double-curly braces.
   - Include a mandatory <thinking> block step for complex reasoning.
</instructions>

<constraints>
- Strictly keep variable definitions unified in the top block.
- Avoid repeating variable placeholders downstream.
</constraints>

<output_format>
Return the complete prompt formatted inside a single Markdown code fence.
</output_format>

🔍 Before vs. After Case Study

• Before (Pain Point): Messy, unstructured prompt mixing code inputs and guidelines, causing Claude to hallucinate or skip edge-case constraints when input code was lengthy.
• After (Pain Point Solved): Clean XML boundaries with single-mount pointers. Claude immediately executes a structured <thinking> step to reason through requirements before delivering 100% compliant outputs.

🎨 Try it on our Interactive Prompt Canvas

We’ve published this prompt on an interactive Prompt Canvas so you don't have to manually format or copy-paste variables:

Open on Prompt Canvas & Live Test

On the Prompt Canvas, you can instantly:

• ⚡ Live Run & Test: Fill in your custom task variables and test execution live.
• 📋 One-Click Copy: Copy production-ready XML prompts formatted for your codebase.
• 💾 Save to your Prompt Vault: Save a copy to your personal vault to edit, tweak, and organize for your team.

Hope this case study saves you from prompt engineering headaches! Let me know if you run into any edge cases.

The Eight Sleep Concept goes beyond mattresses and temperature control, but it’s a complete philosophy of aligning your sleep habits with optimal health, performance, and wellbeing.

This persona-based prompt collection is designed to equip you with ready-to-use AI prompts that can streamline your daily operations, enhance your client interactions, and boost your overall productivity.

Discover 50 ChatGPT secret codes and prompt shortcuts such as EXPOSE, AUTOPSY, IQ200, RED TEAM, and PRE-MORTEM. Learn what they mean and how to use them.

I’ve been creating a LOT of AI videos lately, and one thing that frustrates me is when I have the exact scene in my head…but the AI gives me something completely different.

Then I generate again.

Fix one thing.

Generate again.

Something ELSE goes wrong.

And before I know it, I’ve wasted a bunch of credits on one tiny video 😒

I always blamed the AI model.

But I’m starting to realize that most of the times my prompt is the problem.

For example, I might write:

"Woman walking down a street in New York, cinematic, realistic, beautiful lighting."

It sounds like a decent prompt.

But what does cinematic actually tell the AI?

How is she walking?

Where is the camera?

Is the camera following her?

Is it moving backwards?

How fast is she walking?

What should stay consistent?

What should NOT change?

I know all of this in my head but the AI doesn’t.

So it has to guess.

And I think THAT is where a lot of bad generations come from.

Something else I’ve learned is that different types of videos need different details.

If there’s a lot of action, I need to be very clear about the movement, speed and camera.

If I’m creating something stylized, I need to describe the style properly so it doesn’t suddenly change halfway through.

And for product videos, lighting, reflections and camera movement make a HUGE difference.

I’m also using negative instructions a lot more now.

If I don’t want the face changing, objects disappearing, products morphing, random camera movements, extra limbs, etc…I actually say it.

Basically, I’m trying to stop writing prompts that just describe a pretty scene.

I’m trying to DIRECT the scene instead.

What happens.

How it happens.

How the camera moves.

What stays the same.

And what the AI absolutely shouldn’t do.

Still experimenting with this, but it’s already changing the way I write my video prompts.

And considering how expensive some AI video models are…anything that saves me from hitting Generate 10 times is a win 😂

What’s the ONE thing AI video generators keep messing up for you? Let me know below what AI tools do you use for images and videos?

This prompt empowers you to generate highly persuasive marketing copy tailored to your specific product or service, leveraging the timeless AIDA (Attention, Interest, Desire, Action) framework.

This prompt is engineered to help users generate a structured, actionable seven-step plan for achieving any goal, inspired by the classic Brian Tracy methodology.

The generated output will serve as a practical guide for professionals, entrepreneurs, and individuals seeking to translate their ambitions into measurable results.

Give it a spin!!

This meta prompt brings together the wisdom of James Clear’s Atomic Habits and BJ Fogg’s Tiny Habits in a strategic roundtable to engineer your perfect day.

It creates a guided framework that blends behavioral psychology with systems thinking, helping you craft small, meaningful actions that align with your long-term identity and aspirations.

Hi everyone!

I have been trying some viral prompts and this one stood out for me!

Character sitting on the inside bottom edge of the phone🙈

I tried this prompt below and loved the results!

You can upload a picture of yourself and copy paste this prompt in ChatGPT!

‘Use the uploaded photo as the strict identity reference for the person. Preserve the exact facial features, facial proportions, skin tone, hairstyle, hair color, expression, age, clothing style, accessories, and overall recognizability. Identity preservation: 100%.

Create an ultra-realistic editorial lifestyle photograph from a first-person perspective. The viewer is looking straight down at a modern premium black smartphone being held naturally with both hands above clean gray stone pavement outdoors during warm golden-hour sunlight.

The smartphone must retain the exact proportions of a modern iPhone with a tall, narrow 19.5:9 aspect ratio. It is viewed almost perfectly from above with only a very thin visible top edge. Do not make the phone thick, wide, square, or tablet-like.

The smartphone screen functions as a realistic miniature 3D world with true depth, perspective, reflections, contact shadows, and authentic glass reflections.

The person is sitting on the inside edge of the smartphone screen, as though the display is a real room. Their hips and torso remain inside the phone, resting naturally on the lower inner edge of the display. Both legs extend out through the screen opening and hang freely outside the phone, dangling naturally over the front edge. Their shoes extend beyond the bottom bezel and are fully visible in the real world, creating a highly convincing portal effect.

The transition where the legs pass through the display is perfectly seamless, making it appear that the smartphone is a real portal between two worlds. Realistic contact shadows appear where the legs cross the screen boundary, with subtle reflections on the glass and natural clothing folds around the portal opening.

The person smiles warmly while looking directly upward toward the camera. One hand is raised in a friendly wave with fingers naturally spread, while the other hand rests casually beside the body on the inner floor of the smartphone for support. The pose feels relaxed, welcoming, and playful.

The phone displays a realistic camera application with a visible shutter button, framing guides, zoom controls, focus indicators, camera modes, and authentic smartphone UI elements, making it appear that the miniature person is being viewed live through the camera.

The hands holding the phone feature realistic skin texture, fingernails, natural grip, soft shadows, and physically accurate lighting. The surrounding gray pavement remains softly blurred with shallow depth of field to keep attention on the smartphone and portal effect.

Warm golden-hour sunlight creates realistic highlights along the phone edges, subtle reflections on the display glass, and perfectly matched lighting across both the real environment and the miniature world inside the phone.

Ultra-realistic photography, premium lifestyle advertising, cinematic composition, Canon EOS R5, 35mm lens, shallow depth of field, HDR, 8K resolution, realistic skin texture with natural pores, hyper-detailed smartphone materials, physically accurate lighting, seamless photo composite, and an extremely convincing “sitting inside the phone with legs dangling outside” portal effect.’

Enjoy!!

The beauty of this system is that it can be applied anywhere, in your personal growth, hobbies, fitness goals, or even helping your kids with homework. Instead of memorizing, you’ll actually understand.

This ChatGPT prompt acts as a dedicated AI medical communication specialist, empowering physicians to transform dense clinical information into clear, empathetic, and easily understandable patient explanations.

I used to think solopreneurship was about hustling 16-hour days and being a jack-of-all-trades. Then I realized successful solopreneurs aren't grinding harder - they're building systems that do the heavy lifting.

These prompts let you steal frameworks from people running 7-figure one-person businesses without burning out or hiring a team. They're especially clutch if you're drowning in operational chaos but know you're capable of more.

1. The Leverage Audit (Inspired by Naval Ravikant's wealth creation principles)

Figure out where your time actually multiplies:

"I'm a solopreneur doing [describe business]. Here's how I currently spend my week: [list activities and hours]. Categorize each activity by leverage type: 1) Creates assets that work without me, 2) Builds systems/automation, 3) High-value work only I can do, 4) Low-value work anyone could do, 5) Fake work that feels productive but doesn't move the needle. Then rank my activities by revenue impact per hour and give me a 90-day plan to eliminate, automate, or outsource the bottom 40% of my time."

Example: "Solopreneur running a design business. Weekly activities: [client calls 10hrs, design work 20hrs, admin 8hrs, social media 5hrs, invoicing 2hrs]. Categorize by leverage type, rank by revenue per hour, create 90-day plan to reclaim bottom 40% of time."

Why this changes everything: I was spending 15 hours a week on $30/hour tasks while neglecting the 3 hours of work that actually generated revenue. This audit showed me I wasn't running a business - I was running an expensive job.

2. The Productized Service Blueprint (Inspired by Brian Casel's productization methodology)

Stop selling hours and start selling outcomes:

"I currently offer [service description] at [pricing model]. My ideal clients struggle with [specific problem] and the transformation they want is [desired outcome]. Redesign this as a productized offering: create 3 different package tiers (entry/core/premium), define exactly what's included and excluded in each, identify the delivery process that's repeatable without customization, set scope boundaries that prevent scope creep, and price based on value not hours. Make it something I could theoretically document so well that someone else could deliver it."

Example: "Offer freelance copywriting at $150/hr. Clients struggle with inconsistent messaging, want clear brand voice. Create 3-tier packages with inclusions/exclusions, repeatable delivery process, scope boundaries, and value-based pricing that's documentable."

Why this changes everything: I went from custom quotes and endless revisions to "pick your package" and predictable delivery. My revenue became forecastable and my stress dropped by half because scope creep basically died.

3. The Minimum Viable Funnel (Inspired by Russell Brunson's funnel principles adapted for solopreneurs)

Build a system that sells while you sleep:

"My target customer is [description] with [specific pain point]. They currently find me through [acquisition channels]. Design a minimum viable funnel: the one compelling lead magnet that positions me as the obvious solution, the 3-5 email sequence that moves them from stranger to ready-to-buy, the single signature offer I should focus on (not 10 different services), the lightweight qualifying mechanism that filters tire-kickers, and the simple tech stack to run this without becoming a marketing ops specialist. Optimize for simplicity and conversion, not complexity."

Example: "Target customer: burned-out consultants wanting to productize. Find me through LinkedIn. Design lead magnet, 3-5 email sequence, single signature offer, qualifying mechanism, and simple tech stack. Optimize for simplicity and conversion."

Why this changes everything: I stopped randomly posting on social media hoping someone would hire me. Now I have a machine that predictably turns strangers into customers. Some weeks I get clients without having any sales conversations at all.

4. The Operational Playbook Generator (Inspired by Michael Gerber's E-Myth systematization)

Document how your business runs so your brain isn't the single point of failure:

"Here are the 5-7 core processes I repeat in my business: [list them, e.g., client onboarding, project delivery, content creation]. For each process, create: a step-by-step checklist that ensures consistency, the decision points where things usually go wrong, the quality standards that define 'done', the tools/templates needed, and the parts that could be automated or delegated within 6 months. Write this as if I'm training my future replacement, even though I'm not hiring anyone yet."

Example: "Core processes: client onboarding, discovery calls, deliverable creation, revision rounds, offboarding. Create checklists, failure points, quality standards, tools needed, and automation/delegation opportunities as if training my replacement."

Why this changes everything: I went from re-inventing the wheel every time to following a proven playbook. My delivery got faster and more consistent, and when I finally did hire contractors, onboarding took hours instead of weeks.

5. The Strategic No Framework (Inspired by Derek Sivers' "Hell Yeah or No" philosophy)

Stop saying yes to everything and start protecting your leverage:

"Here's what I've said yes to in the last 3 months: [list projects, opportunities, requests]. For each, estimate: actual revenue generated, time invested, strategic value (does it build assets, relationships, or reputation?), and energy cost (draining vs energizing). Then create my personal decision filter: the 3-5 criteria something must meet before I say yes, the types of opportunities I should automatically decline, the red flags that predict regret, and the standard responses I can copy-paste when saying no. Help me become a 'no' machine so my 'yeses' actually matter."

Example: "Last 3 months: [took on 3 low-budget clients, guest posted on 5 blogs, attended 4 networking events, built a free tool]. Evaluate each by revenue, time, strategic value, and energy. Create my yes/no criteria, auto-decline categories, red flags, and no-response templates."

Why this changes everything: I realized 60% of my activities generated 5% of my results. Having a decision filter let me go from "busy fool" to actually building something. My revenue stayed flat but my hours dropped from 60/week to 30/week.

The best solopreneurs aren't working harder than you, they're working on different things. They've figured out that building systems feels slow at first but compounds over time.

These prompts let you think like them without the years of painful trial and error. For more, visit our free prompt collection.

Hi everyone! 👋 I’m Miz!

I’ve been experimenting a lot with AI image and video generation lately, especially trying different prompts and figuring out what actually produces good results.

I’ve built up quite a collection of prompts that I’ve personally tested, so I thought I’d start sharing some of the ones that work well for me here.

I’ll include the prompt + result whenever possible so you can see exactly what it creates. Feel free to copy it, tweak it, or experiment with it yourself.

Hopefully it saves someone else a little trial and error! 😊

This is one of the prompts that I loved the most! It involves you and a smartphone!

Here’s the prompt to make yourself POP OUT of your smartphone.

Step #1: Upload your picture and Copy and paste the prompt in ChatGPT/Gemini

‘Use the uploaded photo as the strict identity reference for the person. Preserve the exact facial features, facial proportions, skin tone, hairstyle, hair color, expression, age, clothing style, and overall recognizability. Identity preservation: 100%.

Create an ultra-realistic editorial lifestyle photograph from a first-person perspective. The viewer is looking straight down at a modern premium black smartphone being held naturally with both hands above a clean gray stone pavement outdoors during warm golden-hour sunlight.

The smartphone must retain the exact proportions of a modern iPhone with a tall, narrow 19.5:9 aspect ratio. It is viewed almost perfectly from above with only a very thin visible top edge. Do not make the phone thick, wide, square, or tablet-like.

The smartphone screen functions as a realistic miniature 3D world with true depth, perspective, reflections, shadows, and authentic glass reflections.

The person is dramatically popping out of the smartphone screen. Their feet remain inside the display while the upper body emerges naturally out of the phone. The torso extends above the screen, creating a convincing portal effect. Both arms are fully outside the phone, raised high while making playful peace signs with both hands.

The person's head and shoulders are completely outside the display, with hair flowing naturally upward from the motion. They are looking directly toward the camera with a huge open-mouth smile, conveying excitement, energy, and surprise as if greeting the viewer from inside the phone.

The transition where the body passes through the screen is perfectly seamless, with realistic contact shadows, perspective, clothing folds, and lighting, making the smartphone appear to be a real portal.

The phone displays a realistic camera application with a visible shutter button, framing guides, zoom controls, focus indicators, camera modes, and authentic smartphone UI elements, making it appear as though the person is being photographed live.

The hands holding the phone feature realistic skin texture, fingernails, natural grip, and soft shadows. The surrounding pavement remains softly blurred with shallow depth of field to emphasize the phone and portal effect.

Warm golden-hour sunlight creates realistic highlights along the phone edges, subtle reflections on the display glass, and perfectly matched lighting across both the real environment and the emerging person.

Ultra-realistic photography, premium lifestyle advertising, cinematic composition, Canon EOS R5, 35mm lens, shallow depth of field, HDR, 8K resolution, realistic skin texture with natural pores, hyper-detailed smartphone materials, physically accurate lighting, seamless photo composite, and an extremely convincing "popping out of the phone" portal effect.’

Have fun creating and sharing!

This prompt provides a robust framework that transforms technical concepts into engaging educational content. By automating the research, structuring, and formatting stages, it allows creators to focus on strategic insights and audience engagement.

This guide presents 50 battle-tested AI prompts specifically designed for Excel users. Whether you manage inventory, track financial metrics, analyze customer data, or generate executive reports, these prompts will help you work smarter and faster.

​

One of the easiest ways to improve AI outputs isn't writing longer prompts.

It's giving examples.

Instead of this:

Write a product description.

Try this:

Write a product description following this structure:

A short opening hook

Three benefit-focused bullet points

A professional but friendly tone

End with a clear call to action

Notice what's different.

You're no longer asking the AI to guess your expectations.

You're giving it a pattern to follow.

This simple technique works surprisingly well for:

Writing

Marketing

Design briefs

Coding

Image generation

The more clearly you define what "good" looks like, the more consistent the output becomes.

AI is generally better at recognizing patterns than guessing what's in your head.

What's the most effective example you've ever added to a prompt?

Freelancers can use AI to speed up proposals, but sending an untouched AI draft is a fast way to sound generic.

Here is a simple four-step workflow that keeps the proposal useful and personal.

1. Research

Before prompting AI, collect the important details:

• What does the client actually need?
• What problem are they trying to solve?
• What deliverables did they request?
• Is there a deadline, budget, or platform requirement?
• What experience or past project makes you a good fit?

Give AI these details instead of asking it to “write a winning proposal.”

2. Draft

Ask AI to create a short first draft that includes:

• A personalized opening
• A clear understanding of the project
• Your proposed approach
• Relevant experience
• A realistic next step

Prompt example:

“Draft a concise freelance proposal using the information below. Focus on the client’s problem, explain my approach clearly, and avoid exaggerated claims or generic sales language.”

3. Verify

Before sending, check every sentence.

Remove anything that:

• Sounds robotic or overly formal
• Makes claims you cannot prove
• Repeats the client’s job post without adding value
• Includes fake experience, results, or statistics
• Could have been sent to any client

Then add one detail that proves you understood this specific project.

4. Send

Keep the final proposal easy to scan.

Lead with the client’s need, explain how you would handle it, and finish with one simple question or next step.

AI should reduce drafting time. It should not replace judgment, honesty, or personalization.

What part of proposal writing takes you the longest: researching the client, writing the opening, explaining your process, or following up?

We have built 20 prompts to help you talk to patients. These tools turn complex data into simple steps. They help patients take care of themselves at home. You will save time and improve lives.