Glossary · HallucinationAlso called confabulation, made-up outputChecked 27 September 2026
What is an AI hallucination?
A hallucination is output that is fluent, plausible and false. The model is not malfunctioning when it happens: it is predicting likely text, and a likely sentence is not always a true one. It matters most where the output is a fact somebody will act on, such as a name, a price, or an email address.
In one lineFluent, plausible, false. The model is working as designed.
Why it matters to you
Treating this as a model-quality problem leads nowhere useful, because a better model hallucinates less often and more convincingly. The fix is structural: do not let a model be the source of a fact it could invent.
In practice that means extraction is checked against the fetched page. On the WhoFits lead engine nothing a model returns is trusted unless the text is literally present in the page that was fetched, which is what stops an invented email address reaching a send queue. Every extracted email then passes five further checks, including a domain match against the business name.
The same idea drives citation in a retrieval system, and it is why an answer with a source attached is a different product from an answer without one.
If a proposal used this word and did not explain it, ask us.
The first conversation is with the people who would build it. If there is a fit, the Consult comes next.