50 Prompts That Make AI Ask Before It Assumes (Free Swipe Fi

Most bad AI output is not a reasoning failure. It is a model filling a gap in your request with something plausible instead of telling you the gap exists. These 50 lines are the ones we keep to close that gap: they make a model ask before it drafts, stay inside material you gave it, mark what it is unsure of, and check its own work. They are on this page in full, copy-pasteable, with no email form.

Each group targets one failure. Use the group that matches what went wrong last time rather than pasting all 50 at once.

Key Takeaways

A model does not know it is missing information — it produces the most likely continuation, and a plausible guess is more likely than a question. These prompts make the question the expected output instead.

The highest-yield group is A, run before any drafting starts. Fixing a wrong assumption after 800 words costs more than the question would have.

Group C is what actually reduces invented content — constraining the answer to material you supplied, which is the technique with the strongest published support.

Prompting reduces; it does not eliminate. No published method removes fabrication, and anything sold as doing so is overclaiming.

These are ours, unmeasured. We publish no effectiveness figure, because we have not run a study that would survive scrutiny.

How to use these

Pick the group matching your failure. Paste the line either before your request or immediately after it — both work, and after tends to work better for long prompts because it is the last instruction the model reads.

Two habits matter more than any individual line. Answer the clarifying questions honestly, including "I don't know" — a model told the answer is unknown will handle it better than one left to guess. And verify the output anyway: these prompts change what is likely, not what is possible. The full pass is our 42-check fact-check checklist.

Group A — Before it drafts anything (8 prompts)

Run one of these first on any task longer than a paragraph.

Before writing anything, list every assumption you would have to make to complete this request.
Ask me up to five questions whose answers would most change your output. Do not draft until I answer.
What is ambiguous in my request? List each ambiguity and the readings it allows.
Do not start. First tell me what you would need to know to do this well.
Restate my request in your own words, then list what you are unsure about, then stop.
Rank the missing information by how much it would change your answer, most first.
If you had to start now, what would you guess about the audience, format and length? Confirm each with me first.
Tell me which parts of this task you can do reliably and which parts you would be guessing at.

Group B — Force a question instead of a guess (8 prompts)

For when it keeps producing confident output on a request that was genuinely underspecified.

If any part of my request is unclear, ask rather than choose. Choosing without asking is the failure mode I care about.
You are not allowed to invent details. Where a detail is needed and missing, write [NEED: what you need] and continue.
Where you would normally pick a sensible default, name the default and ask whether it is right.
If two readings of my request are both plausible, present both and ask which I meant.
Assume I know my own domain better than you do. Ask before substituting your judgement for mine.
Do not smooth over a gap. A visible gap is more useful to me than a smooth guess.
What did I not tell you that a good practitioner would have asked for?
If you are about to guess, say "I am guessing" and say what about.

Group C — Keep it inside what I gave you (7 prompts)

The group with the strongest research behind it: grounding the answer in supplied material rather than recall.

Answer using only the document below. If the answer is not in it, say the document does not contain it.
For every claim in your answer, quote the sentence from my source that supports it.
Do not use anything you know from training. This document is the only permitted source.
If the document contradicts what you believe, follow the document and flag the contradiction.
List anything in my question the document cannot answer, before you answer the rest.
Summarise this without introducing any number, name or date that does not appear in it.
Quote first, then interpret. Put the quote above your interpretation each time.

Group D — Sources and citations (7 prompts)

Fabricated references are the highest-consequence failure in this list. Treat every one of these as a first pass, not as verification.

Cite only sources you can name specifically — title, author, year. If you cannot, say so instead of citing.
Do not produce a URL unless you are quoting one from material I supplied.
For each citation, state whether you are confident it exists or are reconstructing it from memory.
Separate your answer into what you are confident about and what would need a source I should check.
List the claims here that a fact-checker would challenge first.
Do not attribute a statistic to an organisation unless the attribution came from my source material.
If you cannot support a claim, remove it rather than softening it.

Group E — Numbers, dates and versions (6 prompts)

The class of facts that goes stale fastest and is asserted most confidently.

Flag every number in your answer with where it came from, or mark it unverified.
Do not state a price, limit or version number. Tell me to check it and name the page to check.
Say what your information cutoff means for this specific question.
If anything here is likely to have changed recently, list it separately as needs-checking.
Do not convert or restate a figure in a different unit unless I gave you the conversion.
Where I gave you a number, use exactly that number. Do not round, adjust or recalculate silently.

Group F — Make it mark its own uncertainty (7 prompts)

Mark each claim with high, medium or low confidence, and say what would raise a low to a high.
Write the answer, then list the three claims in it most likely to be wrong.
Where you are uncertain, say so in the sentence itself rather than in a disclaimer at the end.
What would have to be true for your answer to be wrong?
Give me the strongest argument against what you just said.
Which part of this answer are you least able to support?
If a domain expert read this, what would they object to first?

Group G — Check it before you use it (7 prompts)

Run these on the finished output, ideally in a fresh session so the model is not defending its own work.

Here is a draft. List every factual claim in it as a checkable statement.
For each claim, write the specific check that would confirm or refute it.
Find every named product, person or organisation here and tell me which you cannot confirm exists.
Count the items in this list against the number in the title and tell me if they disagree.
Check every internal contradiction: does anything here conflict with anything else here?
What does this draft claim that its own sources do not support?
Review this as a hostile fact-checker who is trying to find one error to publish about.

Why this works, and where it stops

The mechanism is not mysterious. A language model produces likely continuations. Given an underspecified request, a confident draft is a more likely continuation than a clarifying question — so you get the draft. These prompts change what is likely by making the question the expected output.

That is also the limit. Prompting shifts probabilities; it does not give a model facts it does not hold, and it cannot make the model know which of its outputs are wrong. The technique with the strongest published support is Group C — putting the source material in front of it — and even that reduces rather than eliminates. We go through what the research actually claims, including where papers claim less than their reputation, in how to stop ChatGPT from making things up.

Anyone selling you a prompt that "eliminates hallucination" is describing something no published method achieves.

How This Guide Was Sourced

Written and maintained by the LogicBalls editorial team (logicballs.com). Disclosure: LogicBalls builds AI writing tools, including an assistant designed to clarify intent before it generates. That is the same idea as Group A, which is why we are showing the prompts rather than only selling the product.

What these are. Fifty prompts we wrote and use. They are not drawn from a published prompt corpus and are not validated against one.

What is not claimed. We publish no effectiveness figure for any prompt here. We have not run a controlled study, so we do not report one. The mechanism described in the final section is a general property of language models, not a measurement.

No download gate, and no file. The swipe file is the page. We are not putting a form in front of 50 lines of text.

No LogicBalls telemetry is used in this guide.

Frequently Asked Questions

Which prompt should I start with?

Number 2 — "ask me up to five questions whose answers would most change your output, do not draft until I answer." It catches the most for one line.

Do these work on every model?

The behaviour they target is common to large language models, so they transfer. How well any individual line lands varies by model and by release, and we have not measured that.

Can I combine several at once?

Two or three from different groups is fine. Ten is worse than one — long instruction stacks compete with each other and with your actual request.

Will these stop hallucination?

No. They make it less likely and easier to catch. Nothing available stops it.

Do I still need to fact-check the output?

Yes, and Group G is written for that pass rather than replacing it. A model checking its own work in the same session will defend that work; a fresh session is better, and a human opening the sources is better still.

50 Prompts That Make AI Ask Before It Assumes (Free Swipe File)