You ask an AI a question. It answers in fluent, confident prose — complete with a study, a percentage, and a name. Some of it is wrong, and nothing about the wording tells you which part. That's the whole problem with hallucinations: the errors wear the same suit as the facts.
The fix is not "trust it less" in some vague way. The fix is a repeatable audit step between AI wrote it and I used it. Below is a short hallucination checker prompt you can copy right now, a test run showing what it catches and what slips past it, and an honest account of where a one-liner stops being enough.
Not every mistake is a hallucination. A useful working definition: a hallucination is a claim the model states as fact that has no grounding in reality or in your source material. The common shapes:
The dangerous ones are the middle two. Obvious nonsense filters itself; a wrong year in a fluent paragraph does not.
Here is the short version, free, no strings. It works on ChatGPT, Claude, or any capable model — paste it into a fresh chat, then paste the answer you want audited:
Audit the text below for hallucinations. Do not add new information.
1. Extract every factual claim as a separate numbered line.
2. Label each claim: VERIFIABLE (state how to check it),
SUSPECT (state what makes it doubtful), or
FABRICATION-PATTERN (named source/study/number with no citation).
3. Flag every name, number, date, and citation for manual checking.
4. Finish with the 3 claims most likely to be wrong, ranked.
Text to audit: [PASTE THE AI ANSWER HERE]
Eight lines. It won't verify anything for you — no prompt can browse your trust boundaries — but it converts a smooth paragraph into a checklist of discrete, checkable claims. That transformation alone kills the fluency illusion.
I planted three errors in a five-sentence "AI answer" about JavaScript frameworks, mixed with two true claims, and ran the checker on it (one pass, Claude Sonnet, fresh context). The five:
Results — honestly better than I expected:
So the eight-liner went three for three on planted errors. Case closed? Not quite — look at everything else the run produced.
The same pass that caught all three errors also turned my five sentences into nine numbered claims and eight flagged items to check by hand — including SUSPECT labels on soft phrasing like "a lighter alternative" and "widely used," which are opinions, not hallucinations. That's the honest boundary line, and it's about scale:
I got tired of rebuilding that structure by hand every time, so I maintain a full version as a paid prompt: Hallucination Checker & Fact Audit on PromptBase. It runs the audit claim-by-claim with a verdict on each finding, the exact lines to double-check, and how to verify them — the quality-control layer I run on my own AI output before it ships.
Whichever version you use, the audit only pays off if the checking step is fast. The routine that works for me:
You can, and it will apologize and produce a new answer — with new errors. Self-correction without structure is theater: the model has no more access to ground truth on the second pass than it had on the first. What changes the game is forcing the output through an external structure — claim by claim, label by label — which is exactly what the checker prompt does. The structure is the product; the model just fills it in.
This article was written with AI assistance; every claim, date, and test result above was checked by hand. The test run is reproducible — paste the five claims into the checker and compare.
Full audit version: Hallucination Checker & Fact Audit. Next in this series: fact-checking ChatGPT answers end to end.