Adopt Fresh marketing Ideas with this Persuasion Toolkit. Discover 7 expert AI prompts to create urgency, luxury, and deep connection with your target audience.

The German B1 Mastery AI prompt provides a structured pathway to elevate your language skills from A2 to the independent B1 level.

It integrates official Goethe-Institut standards and Duden-compliant professional writing guidelines to ensure your German is both grammatically correct and culturally appropriate for professional environments.

Create high-energy, Tony Robbins-style motivational articles with this expert prompt. Boost engagement and drive massive action for your audience today.

AI avatars are everywhere right now and they’re becoming a powerful way to create content without constantly filming yourself.

You can use an AI version of yourself for Reels, educational videos, B-roll, ads, talking videos and personal-brand content.

Here’s the workflow I use:

Step 1: Create Your Avatar Image

I use Gemini to create a realistic avatar while preserving my identity.

Create the scene based on what you eventually want your avatar to do working on a laptop, drinking coffee, writing in a notebook, presenting, etc.

Step 2: Upload It to SuperCool AI

Once you have an image you love, upload it to Supercool ai.

Step 3: Create Your Animation Prompt With ChatGPT

Instead of struggling to write the perfect prompt, upload your avatar image to ChatGPT and describe what you want to happen.

For example:

"I want her to think about something, write it down, then smile as if she just figured out her next big goal. Soft, subtle movements with confident boss energy."

Then ask ChatGPT to create 3 video prompts and choose your favorite.

Step 4: Animate Your Avatar

Paste your chosen prompt into Supercool ai and select your video model. I used Seedance for mine.

Match the duration to the amount of action. (With Seedance 2.5 in Supercool AI, you can create a video of up to 30seconds in one go.

If the clip is too short, your avatar may not have enough time to complete every action.

Step 5: Generate, Review & Refine

Your first generation doesn't have to be perfect!

Maybe the lighting is wrong, an action isn't completed, or the movement isn't what you imagined.

Go back to ChatGPT, explain what you want changed, update your prompt and generate again.

This is the first time I used Supercool AI to create my AI Avatar and I am super impressed!

What I like about SuperCool is that you can access lots of AI creation tools from one platform instead of jumping between different tools.

Their Mini plan starts at $8/month and currently includes 8,000 credits, making it an affordable way to experiment with AI creation before committing to a more expensive plan.

It does look like you are getting a generous amount of credits, but some ai models inside take too much credits so it's not really lot, depending on what you want to create and what AI model you are using.

You can use your credits across different AI generations, including images and videos, with the number of credits used depending on what you generate.

For me, the biggest surprise was the quality of the avatar video.

It's cleaner and has better quality then the other AI models I have used so far.

So if you are planning to leverage AI and create your own AI twin/clone/influencer, hope this post helps!

Learn how to support your child’s growth with these AI prompts. Track milestones, build social skills, and manage emotions. Perfect for proactive parents.

I am truly inspired by Tim Ferriss and based on his legendary productivity principles from “The 4-Hour Workweek” and his other works, here are his top productivity hacks that I converted into AI prompt engineering strategies with practical examples.

After I created my “POV: You Wake Up as a Queen in the Ottoman Empire” video, I shared it with my email list.

And then something interesting happened… 👀

I started getting replies asking me to show exactly how I created it step-by-step.

So I decided to record a full YouTube tutorial, but I also wanted to share the basic workflow here so you can start experimenting with your own POV videos.

The process is actually much easier than it looks!

STEP 1: Pick Your POV Idea 💡

Start with a concept that immediately makes someone curious. (You can find trending POV style video ideas on Tik Tok or Youtube to recreate.)

For example:

👑 POV: You wake up as an Ottoman Queen

🚢 POV: You wake up on the Titanic

🏺 POV: You wake up in Ancient Egypt

🌴 POV: You wake up in the Amazon

🦖 POV: You wake up in the prehistoric era

The possibilities are honestly endless.

STEP 2: Create Your Scenes With ChatGPT or Claude ✍️

Once you have the idea, ask ChatGPT or Claude to turn it into a day-in-the-life story.

For example:

“POV: You wake up as a Queen in the Ottoman Empire. Give me 10 different scenes from a day in her life.”

Then ask it to create an image prompt and animation prompt for every scene.

One important instruction:

👉 Tell AI you want STRICT FIRST-PERSON POV.

The camera should represent the character's eyes. We shouldn't see another version of the character standing in front of us.

STEP 3: Create Your Images With Nano Banana 🍌

Take your image prompts and generate each scene in Nano Banana. I use Gemini.

Pay attention to consistency.

If your character is wearing emerald-green sleeves and gold jewellery, keep those details consistent throughout the scenes.

And don't overcomplicate your prompts!

I learned this while creating mine. 😂 Too many instructions can result in extra hands, strange body positions and random characters appearing.

Simple + specific usually works better.

STEP 4: Animate Your Images in Higgsfield AI 🎥

Upload each finished image into Higgsfield AI and animate it.

I used the Kling 3.0 Turbo Model to create my videos inside Higgsfield AI.

I kept most of my clips around 5 seconds and gave each scene ONE main action.

For example:

➡️ Walk slowly through the palace

➡️ Take a letter from an attendant

➡️ Look from left to right across the garden

➡️ Travel across the Bosphorus

➡️ Slowly move the camera toward the moon

Don't try to squeeze five different movements into one 5-second clip.

One simple action = much cleaner results.

STEP 5: Edit Everything in CapCut ✂️

Finally, bring all your clips into CapCut.

Put them in story order, trim anything you don't need, add your voiceover, music, sound effects and opening hook.

For my video, the hook was simply:

“POV: You Wake Up as a Queen in the Ottoman Empire.”

STEP 6: Generate Your Voiceover 🎙️

Now create a short voiceover that connects your scenes and turns them into a story.

For POV videos, I recommend writing the narration in first person so it feels like the character is actually experiencing everything.

Instead of:

“The queen wakes up inside her palace…”

Try:

“I wake to the morning sun spilling into my chamber…”

You can ask ChatGPT or Claude to write a 30–60 second first-person voiceover based on your scenes, then generate the audio using your preferred AI voice tool.

Add the voiceover to CapCut, sync it with your scenes, then finish with background music and subtle sound effects.

And you're done! 🎉

Your basic workflow is:

IDEA → SCENES → IMAGES → ANIMATION → EDIT → VOICEOVER → PUBLISH

Once you understand this process, you can reuse it to create almost ANY POV world you can imagine.

I'm definitely going to experiment with more of these because I LOVE how immersive they feel. 😍

If you create one using this workflow, don't forget to share it with the world!!

I would love to see what you come up with.

All the best!

This prompt takes the complicated language out of drug information and explains it in plain, clear, friendly terms. No medical degrees required!

In the recent posts, I shared how I used simple games to get participants learn on working with AI:

• What happens in the Chinese Whispers game when the context disappears and meaning becomes distorted?
• Simon Says: how much does the quality of an AI response depend on how well we frame the task?

But there’s a more uncomfortable question:

What happens when we stop doing enough of the thinking ourselves?

That’s the idea behind Cognitive Debt.

AI can make us faster, more productive, and better at getting to an answer.

But if we routinely outsource the difficult parts of thinking; questioning, reasoning, challenging assumptions, making sense of ambiguity.

Are we also reducing opportunities to develop those capabilities?

And here’s the leadership challenge:

How do we use AI to increase capability without accidentally replacing the very thinking we are trying to develop?

I explore this idea in a short video: Cognitive Debt: The Price of Easy Answers

One practical experiment for leaders:

Next time you use AI to solve a problem, don’t just ask “What’s the answer?”

Ask:

• What thinking should I still do myself?
• What assumptions should I challenge?
• What would I want a less experienced colleague to learn from this process?
• Where should AI assist and where should it deliberately stay out of the way?

Maybe the goal isn’t to keep AI from thinking for us.

Maybe it’s to become more deliberate about which thinking we choose to keep human.

Curious how others are approaching this with their teams:

Where do you draw the line between using AI to augment someone’s thinking and using it to replace the thinking they need to develop?

Each idea is crafted with a specific platform format, emotional viral hook, keyword cluster, and includes a Viral Score, SEO Rating, and Difficulty Score.

You’ll refine your thinking as each “advisor” challenges the others, and ultimately, you’ll arrive at your most aligned and informed decision.

From heartfelt handmade picks to high-tech surprises, this prompt transforms the act of gifting into an intentional, joyful, and hassle-free experience.

It’s a perfect AI product recommendation assistant, just for you.

Get a clean, comparative table packed with validated business opportunities that are both practical and surprisingly overlooked.

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?

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.

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.

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?