Most people assume their prompts are clear. Write the instruction, hit send, get a response. If the output is off, tweak a word, try again. But here’s what’s actually happening: every prompt you write is full of gaps. AI fills those gaps silently, with its own assumptions. You never see them. You just see the output and wonder why it missed the mark. A Redditor in r/PromptEngineering just shared a dead-simple trick for making those hidden assumptions visible. And once you see it, you can’t unsee it.
The Old Way vs. the New Way
The old way: write a prompt, read the output, guess what went wrong, edit blindly. You might iterate five or six times and still not fix the actual problem, because the actual problem was never visible to begin with.
The new way: write a prompt, let AI interrogate you about it, then have it rewrite the prompt from scratch. Compare the two versions. The difference tells you everything. It sounds almost too simple. But the insight it surfaces is real.
🔍 What’s Actually Going On
When you write a prompt, especially a detailed one, you’re making dozens of tiny assumptions. How formal should the output be? What context should AI rely on? What format? What to ignore? Most of those assumptions stay in your head. They never make it into the prompt. AI fills those gaps with its best guesses. Say you ask it to “write a summary for our team.” It guesses: which team? How long? Technical or plain language? What counts as important? It picks answers, proceeds, and you get something that feels slightly off without knowing exactly why.
This trick flips that dynamic. It forces AI to surface its own uncertainty before it proceeds. That’s the whole key.
The Steps (Reproduce Exactly)
Here’s how the original poster laid it out:
- Write a prompt on any subject. Longer is better, more surface area means more hidden assumptions.
- At the very end of your prompt, paste these two lines exactly: “Pause to ask me questions about ambiguous issues. Before starting our conversation ask me any questions you need to resolve ambiguity. Ask questions one at a time and pause for my answer.” “When done create a new prompt that resolves all questions.”
- Answer AI’s questions one by one. Don’t rush. Each question it asks is revealing a gap you didn’t know was there. A well-constructed prompt might generate three to eight clarifying questions, every single one is a place where you were silently relying on AI to guess correctly.
- Once it builds the new prompt, compare the two side by side. Look at:
- ✏️ What it added that wasn’t in your original
- What it removed or restructured
- How it handled formatting, tone, and constraints
- What context it baked in from your answers
The gap between your original and the AI-generated version is a map of your blind spots.
Why It Actually Works
This is essentially a structured ambiguity audit. By asking AI to resolve uncertainty before responding, you’re making it externalize its reasoning. Instead of silently picking an interpretation, it tells you what it was going to guess, and asks if it got it right. The AI-written prompt isn’t better because AI is smarter than you. It’s better because the question-and-answer process filled in gaps you didn’t know to fill.
Think of it as a mirror for your prompt-writing habits. You’ll notice patterns fast: maybe you always forget to specify output format, assume context AI doesn’t have, or leave tone completely undefined. Once you spot the recurring blind spots, fixing them becomes muscle memory rather than guesswork.
⚡ Where This Is Most Useful
This technique pays off most when:
- You’re building a complex system prompt for an agent or assistant
- You keep getting outputs that are “close but not quite right” and can’t figure out why
- You’re handing a prompt to someone else and want it to be airtight
- You’re building a reusable prompt library and want well-defined, consistent templates
Taking It Further
Once you’ve run this exercise a few times, you’ll start catching the gaps before AI does. That’s the real payoff. You develop a feel for what “unambiguous” actually looks like, and your first drafts get sharper as a result. You can also use the AI-generated prompt as a baseline, then trim it down to find the minimum version that still produces good output. That teaches you which details actually matter versus which ones AI was inferring correctly all along.
Over time, you stop second-guessing your prompts and start shipping them with confidence. The original post is short, but the idea is solid. Head over to the r/PromptEngineering thread to check it out and try it on your next prompt before you send it.
A trick to see what ai uses in a prompt
by u/External_Word9887 in PromptEngineering