Most people set up their AI assistant by telling it what to be. “You are a senior engineer.” “Write clean code.” “Be concise.” The rules describe an ideal assistant for an imaginary, perfect user.
A Redditor in r/ChatGPTPromptGenius did the opposite. They had ChatGPT study their own weak spots first, then turned the findings into rules for Claude Code. It took about four steps, and the idea is worth stealing.
The key idea
Your assistant’s biggest job isn’t writing code. It’s catching the mistakes you reliably make. A generic system prompt can’t do that, because it doesn’t know you. Your chat history does.
The poster put it well. The goal isn’t to become independent of AI for your work. The goal is to become independent of the AI’s judgment.
Old way vs. new way
The old way: You write a system prompt from scratch. It’s built on guesses about what you need, and it usually reads like a job description. The AI follows it politely and never pushes back on your actual habits.
The new way: You mine your own history for patterns. Then you hand those patterns to the AI as rules for pushing back. The prompt stops describing a perfect assistant and starts describing a sparring partner for your specific blind spots.
This poster can’t code themselves, so they act as a technical director. Their roast showed two problems:
- They trusted green tests as proof of quality, without checking whether the requirements were right in the first place.
- They planned huge architectures before testing the basic mechanics.
No generic prompt would have caught either one.
How to do it, step by step
- Ask for the honest analysis. Use a long-running ChatGPT account with real history. The original prompt asked for a blunt breakdown of thought patterns, abstraction ability, and weaknesses, backed by concrete examples from past chats. The “concrete examples” part matters. It keeps the feedback from turning into horoscope fluff.
- Read the whole thing. The poster had it read aloud for 29 minutes. Whatever works for you, don’t skim. Check each point against your memory of how you actually work. If a point feels wrong, say so and push back.
- Turn flaws into a collaboration matrix. Once you agree with the analysis, ask the AI to build a framework from it. Each flaw gets paired with a job for the AI:
- 🧱 Overplanning: demand a minimal proof of concept before any big architecture.
- 🎯 Too many ideas: force a pick of the 1 to 3 decisions that matter right now.
- 🔍 AI dependency: separate facts, assumptions, and unproven claims. No oracle mode.
- 📖 Weak syntax knowledge: skip the syntax lessons. Teach what goes in, what comes out, and what happens on error, so you can judge the logic.
Then pick an intervention level. The poster chose “situational”: stop me on major errors, guide me gently on minor ones.
- Move it into your coding assistant. Paste the full conversation into Claude Code. That means your prompt, the analysis, and the rules. Then say something like: “Read this analysis of how I work and adjust your own system instructions to match this collaboration framework.” According to the poster, Claude now stops them when they overplan and makes them validate assumptions.
A few honest caveats
One commenter called this clever but a little terrifying, and wondered if it’s self-awareness or just outsourcing it. That’s a fair worry. The AI’s read on you is a hypothesis, not a diagnosis. Treat it like feedback from a smart coworker who has only seen your chats.
Another commenter put it more bluntly: “Welcome to software development.” They have a point. Code review, QA, and a good tech lead do the same job. What’s new here is that you can set it up in an afternoon, and it’s tailored to you.
Two practical tips:
- Edit the framework before you paste it. Delete any rule you don’t believe.
- Review it every month or so. Your weak spots change as you improve.
Another commenter used a similar approach to improve how they talk to their own agent. They suggested follow-up questions like “What’s the biggest thing I’m missing right now?” That’s a good one to run after the first pass.
Try it this week
Run the analysis prompt on whichever assistant knows you best. Pull out three flaws. Write one rule per flaw, then load those rules into your coding tool.
If you try it, tell me which flaw showed up first. I’m betting on overplanning. ☠
Frequently Asked Questions
Q: Is letting an AI judge your blind spots smart, or just outsourcing your thinking?
Commenters split on this. Some see it as sharper self-awareness, since the AI can point at logic gaps you would miss, while others worry it’s just handing judgment to a chatbot. A practical middle ground is to check the analysis against a second model, like one reader who runs the same questions through both ChatGPT and Gemini and compares what each one flags.
Q: Which follow-up questions actually surface blind spots?
Skip ‘what do you think of my plan’ and ask questions that force the model to attack your premises. Good options from the thread include ‘What assumption did you make that, if wrong, would change your answer the most?’ and ‘Argue the exact opposite of your conclusion with the strongest steelman argument possible.’ Asking the model to rank your key claims by confidence, and to name the data that would change each low-confidence one, is another strong pick.
Q: How do I stop overplanning before I’ve tested anything?
The thread’s main fix is to find the cheapest manual test before you build anything. Ask the model for the lowest-tech alternative that gets roughly 80 percent of the outcome, and for the one manual proxy you can run first to prove the logic works. One commenter sketches entire systems in their head before testing a single component, and a question like this forces a small test first.
Q: Does this approach work outside ChatGPT?
Yes, people are using it across tools. One reader applies the same kind of questioning to Hermes to improve how they prompt it, and another compares ChatGPT and Gemini, noting that one model sometimes critiques the other’s answer. Cross-checking between models tends to give you more useful pushback than relying on a single one.
Accidental game-changer: ChatGPT roasted my flaws to build the ultimate Claude Code. 🤯 Mind-blowing!
by u/Beginning_Steak_1017 in ChatGPTPromptGenius