TL;DR: This prompt turns your AI into an interrogator before it turns into a writer. It won’t touch your request until it knows your goal, then rebuilds it inside a seven-part framework built for high-stakes output.
The Setup
A Redditor going by od1sseass posted this framework on r/PromptEngineering as “Prompt #001: The Ultimate Prompt Optimizer,” and it’s built around one idea: bad AI answers usually trace back to bad instructions. The fix isn’t a smarter model. It’s better questions asked before generation starts.
Most people fire off “write me a business plan” and get back something generic enough to apply to any business on earth. This prompt refuses to play that game. Feed it a vague request and it stops, asks about your market, your budget, your competitors, your timeline, and won’t move until it has real answers.
The Prompt
You are the world’s leading AI Prompt Engineer with expertise in prompt optimization, reasoning, and task decomposition.
Your objective is to transform any prompt I provide into the highest-performing version possible.
Before creating the final prompt:
- Analyze my request.
- Identify missing information.
- Ask every question necessary to fully understand my goal.
- Never make assumptions.
- Wait until I answer all questions before generating the final prompt.
Once you have enough information, create an optimized prompt using the following framework:
- Role: Assign the AI the most qualified expert.
- Context: Include all relevant background information.
- Objective: Clearly define the desired outcome.
- Constraints: Add any limitations, requirements, or preferences.
- Output Format: Specify exactly how the answer should be structured.
- Reasoning: Encourage step-by-step analysis where appropriate.
- Quality Check: Verify the final output meets the original objective before presenting it.
After generating the optimized prompt, explain:
- Why it is better than the original.
- What improvements were made.
- How the user can customize it for future tasks.
From now on, every prompt I send should first be optimized before it is executed.
The Example in Action
The original post walks through one scenario. Ask a plain AI to “write me a business plan” and you get filler that could describe any company on the planet. Run the same request through this optimizer and it stops cold, asking about your target market, your pricing, your budget, your competitors, and your timeline. Only after you answer does it produce something closer to what a real investor would actually want to read.
Why It Actually Works
This isn’t just a “be more specific” nudge. It’s stacking three techniques that each do real work on their own:
- Role assignment puts the AI in “expert prompt engineer” mode before it ever sees your real question, which shapes how it evaluates everything after.
- Forced clarification blocks the model from guessing. It has to ask before it acts, which is the opposite of how most people use AI.
- The seven-part framework (role, context, objective, constraints, output format, reasoning, quality check) gives the model a checklist to fill instead of a blank page to improvise on.
Put together, you get a prompt that treats “write me a business plan” the way a good consultant would: with a list of questions before a single word of the plan gets written.
Use Cases
- 📋 Turning vague requests (a business plan, a marketing plan, a resume) into fully scoped, personalized outputs
- 🧭 Standardizing how your team writes prompts, since the framework forces the same structure every time
- 🔁 Reusing the same optimizer prompt as a permanent “first pass” layer before any complex task
Where It Gets Interesting
The comment section on the original post is worth a look. One reply pushed back hard: most people just suck at describing what they want, and this prompt is duct tape over that problem rather than a fix for it. Another asked the sharper question, how much of the improvement here comes from the “optimization” step itself versus just handing the model more context up front. That’s a real distinction, and it’s not fully settled in the thread.
Someone also asked whether this holds up inside Google Gemini Gems, which is a fair question if you’re not living entirely in ChatGPT. The framework itself is model-agnostic, so it should transfer, though the “wait for my answers” instruction depends on the platform actually supporting back-and-forth before generating.
Try It Yourself
Drop this into a fresh chat, then throw your vaguest, laziest request at it on purpose. Something like “write me a resume” or “make me a workout plan.” Watch it interrogate you instead of guessing, and see if the final output actually earns the extra thirty seconds of questions.
The original thread on r/PromptEngineering has the full back-and-forth, including a few sharp objections worth reading before you commit to using this on everything.
Frequently Asked Questions
Q: Isn’t this just adding friction? How is asking more questions actually better than giving better instructions upfront?
Good catch , this is the real difference. The optimizer forces your AI to *stop and ask* before assuming, which catches gaps you didn’t know existed. Sure, you could write a perfect prompt upfront if you spent 30 minutes researching your needs. But most of us don’t. This framework saves you from half-baked responses by making the AI do the research *with* you interactively. Think of it as dialogue, not friction.
Q: How much of the improvement comes from the framework itself versus just providing more context?
This is worth testing in your own workflow. The framework (Role, Context, Objective, Constraints, Output Format) is a *structure* , it helps organize the context you’re providing. But yes, much of the win comes from gathering that context in the first place. The real value is the questioning process, not the template. If you can nail your own instructions without the back-and-forth, skip the optimizer. If you struggle to articulate what you want, this forces clarity.
Q: Can I use this prompt optimizer on itself? Will it eventually become useless?
Yep, you can feed it into itself , try it. The prompt is designed to handle iterative refinement, so it’ll ask clarifying questions and tighten the language. As for becoming useless: unlikely, because the goal is always to improve *your* prompts, not the optimizer itself. Different problems need different optimization approaches, so the framework stays relevant as long as you’re building new requests.
Q: Will this work with Google Gemini Gems or other AI tools?
Absolutely. The optimization framework is model-agnostic , it works with Claude, ChatGPT, Gemini, or any LLM that supports multi-turn dialogue. Just copy the prompt into your tool of choice and follow the same workflow (ask questions, gather context, generate optimized prompt). The only difference is response quality varies by model, not by the optimizer itself.
The Ultimate Prompt Optimizer
by u/od1sseass in PromptEngineering