AI Hallucination
An AI hallucination is when a language model states something false as if it were true, presenting fabricated facts, citations, or details with the same confident tone it uses for correct answers, because the model predicts plausible text rather than retrieving verified information.
Hallucination happens because a language model generates the next most likely words, not the most accurate ones. When the model has no real information about a question, it does not stop: it produces a fluent, confident guess that can include invented product specs, fake quotes, or sources that do not exist. The output reads as authoritative regardless of whether it is grounded in anything real.
The most reliable defence is grounding the model in retrieved sources at answer time, the approach behind retrieval-augmented generation. When an AI search system pulls in real documents and answers only from them, hallucination drops sharply, because the model is summarising evidence rather than inventing it. This is also why what a model says about your brand depends heavily on what it can find: if accurate, corroborated information about your products is readily retrievable, the model has something true to ground on instead of guessing.
Hallucination is never fully eliminated, only reduced, so treat any unsourced AI claim about pricing, availability, or specifications as unverified until checked against a primary source. For brands, the honest takeaway is that you cannot control what a model invents in a vacuum, but you can shrink the vacuum by making real, citable information easy to find.