What is an AI hallucination?
A hallucination is output from a language model that is fluent, confident, and false. It arises because such models are trained to produce plausible continuations of text, not to verify claims — so a fabricated citation is generated by the same mechanism as a correct one, and looks identical.
This is why fluency is a poor proxy for accuracy. The wrong answer arrives in the same authoritative register as the right one, and nothing in the output signals which is which.
Hallucination cannot be eliminated, but it can be constrained. Grounding answers in retrieved documents narrows the model's latitude to invent; requiring citations makes claims checkable; and evaluation suites measure how often it happens so the rate is a tracked number rather than an anecdote. Systems handling consequential decisions add human review on top.
Where this shows up in our work
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