Try this right now. Open your AI of choice and paste in a draft you wrote. An email, a pitch, a plan, anything. Ask: “Does this look good?”
It’ll say yes. It almost always says yes.
Now run the second test below and watch the same draft get taken apart. Same AI, same draft, totally different answer. The only thing that changed was the guardrail.
A Redditor on r/PromptEngineering (u/mo-reewy, 40 upvotes) shared 10 prompts they built by trial and error. They say they’re no expert. A few of them are still better than most prompt packs I’ve seen. Let’s test the best ones.
🧪 The 2-Minute Challenge
Step 1: Ask the polite question. Paste your draft and type: “Does this look good?” Save the answer. Notice how many compliments show up before any actual criticism.
Step 2: Run the auditor. Start a fresh chat and paste this, swapping in your draft:
Act as a strict auditor with no incentive to be polite. Review [document] against: accuracy, logic, missing evidence, risk, clarity, feasibility. For each weakness, state the problem, rate it (Critical / Major / Minor), and give a specific fix. Do not praise. If something is fine, say “no issue.” End with the 3 issues that most threaten the outcome.
The author says this caught a math error in a proposal that “does this look good?” had happily approved. Ouch.
Step 3: Make it argue with itself. Got a decision you keep going back and forth on? Try the two advisors:
Act as two advisors analyzing [decision]. Advisor A argues for [option 1] as a [role with a stake]. Advisor B argues for [option 2] as a different [role with a stake]. Each gives 3 arguments with reasoning. Then write a one-paragraph reconciliation stating which arguments survive scrutiny and what recommendation follows. Flag any claim that needs verification.
The author used it on “should I quit my job?” It didn’t say quit. It said ask for a raise first. They did, and they got it.
Step 4: Attack your own plan.
Argue against this plan as a well-informed skeptic who wants it to succeed: [plan]. Give the 3 strongest objections, each with the mechanism by which it causes failure and the evidence behind it. Distinguish “likely to fail” from “could fail.” Then state what would have to be true for the plan to work despite each objection. Do not invent facts.
This one reportedly killed a Notion template idea nobody asked for. Your friends won’t tell you your idea is weak. Your AI will, if you ask it right.
🔍 What Your Results Mean
- The auditor found something Critical: Good. That’s the whole point. Fix it before anyone else sees it.
- The auditor found nothing: Suspicious. Rerun it on a different model, or tighten the criteria. Sometimes your draft is solid, but check before you believe it.
- The two advisors just listed pros and cons: You gave them no real stake. The commenters flagged this too. The prompt only works when the advisors have conflicting incentives, like a CFO versus a head of product.
- The skeptic’s objections feel obvious: Then you already knew the weak spots. Now you have a mechanism and a test for each one.
🛠️ Extra Tips
The whole list works for one reason, and a commenter nailed it: each prompt removes the model’s escape hatch. No “it depends,” no flattery, no filler.
The other seven are worth stealing too:
- Just answer me: Answer in the first sentence, no greetings, no restating the question. If it depends, name the deciding variable.
- Explain it to my mom: Tell it what the person already knows, what they don’t, and what they need to do afterward. “Explain like I’m 5” is too vague.
- What actually matters: Find the 20% that drives 80% of the result, then list what it deliberately left out.
- Assume I’m wrong: Make it list the hidden assumptions in your question before answering.
- Quote it or drop it: Answer only from the source you gave it, quote the exact line, or say “Not stated.” A top commenter called this the strongest of the ten, because it gives the model a legitimate way to say nothing.
- Five whys: Label each cause as EVIDENCE or HYPOTHESIS and stop when it turns into guessing.
- What would I regret: Past you, present you, and future you all weigh in.
Two honest caveats from the comments. First, in the safe/risky and EVIDENCE/HYPOTHESIS prompts, the same model writes the labels and grades them. So treat those labels as hints, not proof. Second, a good upgrade is a rejection step: ask for 3 options, then make it say which two it would kill and exactly why.
And don’t load all 10 at once. Save them as separate notes and pick one per problem. One tool for one job.
🏴☠️ Your Move
Pick one prompt, run it on something real today, and tell me which one humbled you first. My bet is the auditor. Mine always is!
Frequently Asked Questions
Q: Do I save all 10 and pick one, or run them all on the same question?
Each prompt is built for one kind of job, so pick the one that matches the decision in front of you. Running all ten on the same question usually gives you a pile of overlapping answers that are harder to act on.
Q: Can I trust the model’s own labels, like “safe” or “evidence vs. hypothesis”?
Not blindly. The same model that might be wrong is doing the labeling, so a confident “safe” can look as trustworthy as a verified one. One commenter suggests testing the prompt on a document where you already know the hidden flaw, then checking how often the label comes back right.
Q: How do I stop the model from taking the easy way out?
One user adds a rejection step: ask for 3 options, then make it name the 2 it would kill and explain why. Another asks for the 5 questions it needs answered before giving advice, which cuts down on generic “it depends” replies before they start.
Q: Should I always push root-cause analysis to five “whys”?
Opinions split here. Some users like the forced depth because it stops the model at the first plausible cause. Others warn that a fixed number can turn a reasonable hypothesis into a chain of speculation, so they have it re-check the evidence for each cause and allow it to stop at 1, 2, or 3 when the evidence runs out.
Q: Does the “Not stated” option really reduce hallucinations?
Commenters say it helps a lot with contracts and source-heavy documents, because it gives the model a legitimate way to say nothing. One suggestion is to also ask for the surrounding context whenever a quote could be misleading on its own.
I’m not an expert. I learned this stuff on my own, Here are 10 prompts I actually use. Tell me if they’re trash.
by u/mo-reewy in PromptEngineering