All terms
Hallucination
When an AI model states something false or fabricated as if it were fact.
Catching hallucinations is a core part of evaluation work: does the model cite a paper that doesn't exist, invent a legal precedent, or state a wrong number with total confidence? Flagging hallucinations accurately is exactly why domain expertise matters - a non-expert often can't tell a confident wrong answer from a correct one.
Example
An AI cites "Smith et al., 2019" as the source for a claim, but no such paper exists - a domain expert catches it because a non-expert would have no reason to check.