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.
What counts as an AI hallucination?
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:
Fabricated citations — a named study, expert, or paper that doesn't exist. Often dressed with a year and an institution.
