Last week I asked Claude to help me write a pricing page. It gave me something polished, professional, and completely wrong for my audience. The copy was clean, the structure was solid, and it was aimed at the wrong person entirely. I had given it the brief, it had done the work, and we were both operating on totally different assumptions without realizing it.
Sound familiar? That is the problem this prompt solves.
A Redditor going by u/Dramatic-Air9976 shared a dead-simple technique in r/PromptEngineering, and it is one of those things that feels obvious in hindsight but changes how you work with AI once you start using it.
🎯 Why most AI conversations go sideways
Here is what actually happens when you send a prompt: you ask a question, the model fills in the gaps with assumptions, and then it confidently delivers an answer based on those assumptions. Some of them are right. Some of them are very wrong. And you usually have no idea which is which until you have already wasted time on the output.
The fix is not a better prompt. It is a smarter process. Before the AI answers anything, you make it surface its assumptions first.
This one change shifts the AI from “guess and go” mode into “understand before acting” mode. The difference in output quality is significant. Think of it like a good doctor who runs a few questions before prescribing instead of just handing you a pill at the door. You would not trust the second doctor. Do not trust the AI equivalent either.
📋 The exact prompt to use
Here is what the original poster shared, reproduced exactly as written:
Don’t answer my question yet. First do this:
1. Tell me what assumptions I’m making…
2. Tell me what information would significantly change your answer…
3. Tell me the most common mistake people make… Then ask me the one question that would make your answer actually useful… Only after I answer, give me the output
Four moves, one instruction. Here is why each part pulls its weight.
Tell me what assumptions I am making forces the AI to make its hidden reasoning visible. You find out what it thinks you mean before it acts on that interpretation. Invaluable when your question has any ambiguity at all.
What information would significantly change your answer flips the direction beautifully. Instead of you guessing what context to provide, the AI tells you exactly what it needs to give you something useful. This alone has saved me multiple back-and-forth rounds that used to eat up twenty minutes.
The most common mistake people make is free consulting. The AI surfaces pitfalls relevant to your exact situation before you stumble into them.
Ask me the one question that would make your answer actually useful is the closer. One focused question. Not five. Not a questionnaire. One thing that unlocks the whole answer. You answer it, then the AI finally delivers.
💡 Tips and tricks for getting the most out of it
A few things worth knowing before you go try this:
- Paste it before your actual question. Use it as a preamble, then add your real prompt below. The AI reads the whole thing before responding.
- Save it for complex questions. For simple factual lookups, this adds friction you do not need. Reserve it for strategy calls, pricing decisions, technical architecture, or anything where a wrong assumption costs you real time.
- Pay attention to the clarifying question. That is where the gold is. If the AI asks something you do not have a clear answer to, that is your cue to think before you proceed. Treat it like a forcing function for your own clarity.
- Try this variation for deeper results: Add “Also tell me what the smartest expert in this field would consider that I have not mentioned.” It surfaces context you did not know to ask for.
- Use it when you feel stuck. If you have been going back and forth with an AI and not getting what you need, this prompt resets the conversation in the right direction.
🚀 Give it a shot
Copy the prompt above, paste it before your next meaty question, and see what assumptions come back. You might be surprised by what the AI was about to silently run with.
And if you want to dig into the thread itself, the comments on r/PromptEngineering go into some interesting territory around getting consistent AI behavior across different prompts. Worth a read if you are going deeper on prompt engineering.
Frequently Asked Questions
Q: How do I get consistent behavior across different prompts without optimizing each one individually?
Ground your prompts in established frameworks and terminology instead of making up your own rules. When you anchor the AI to canonical theories and proper vocabulary, it tends to apply the same thinking across different contexts. Some models like Gemini 3 and KIMI 2.5 follow structured prompts more reliably than others, so your model choice makes a difference.
Q: What does “grounding” actually mean in the context of prompting?
It’s basically telling the AI to use real, verifiable concepts instead of making stuff up. So instead of inventing a custom framework, you reference established ones (5 Whys, MOSA, Porter’s Five Forces, etc.). Sounds simple, but it works: your answers come out less hallucinated and more credible because the model is leaning on actual knowledge rather than improvisation.
Q: Do I need simple or detailed, structured prompts?
Simple prompts give the model breathing room and work great for quick answers, but detailed prompts (like Deconstruct → Ground → Synthesize → Reframe) lock in consistency and higher quality if you’re using them repeatedly. Simple is faster to write; structured takes more setup but usually pays off if you care about predictable results.
Q: Which models actually follow detailed prompts reliably?
From users testing different platforms, Gemini 3 (Flash and Pro versions) and KIMI 2.5 tend to follow structured, multi-step prompts closely without cutting corners. ChatGPT sometimes returns inconsistent results with detailed prompts, depending on how they’ve been written. If consistency matters to you, test with a few models and you might be surprised by the results.
How to 10x your prompt results
by u/Dramatic-Air9976 in PromptEngineering