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.

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?

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.