AI Hallucination, explained
An AI hallucination is when a model states something false as if it were true, like a fake citation or invented fact, because it predicts plausible text rather than verified facts.
A hallucination is when an AI confidently produces something that is simply not true: a made-up statistic, a quote nobody said, a citation for a paper that does not exist. It is not lying in any human sense; it is doing exactly what it always does, predicting plausible-sounding text, and sometimes the most plausible text is wrong.
This comes straight from the mechanism. A language model predicts likely words, and nothing in that process checks the result against reality. So the more obscure or specific the fact, the more likely it is to fill the gap with something that looks right.
The fix is not to avoid AI; it is to verify. Ask it to show its sources, check anything factual, and treat confident specifics, especially names, numbers, and citations, as claims to confirm rather than facts to trust.
Go deeper
Wield's AI Foundations track covers this hands-on, in plain English, with real examples and a copy-paste prompt to try it yourself.
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