Hallucination
An AI answer that is factually wrong but stated fluently and confidently, as if it were true.
What is an AI hallucination?
A hallucination is an AI answer that is factually false yet expressed fluently and confidently, as if true. It happens when a model fills gaps from its parametric memory instead of grounding the claim in a real source.
A hallucination is dangerous precisely because it sounds right: the model is most fluent exactly where it is least certain.
Language models generate plausible text, not verified fact. When the answer is not anchored to a retrieved source, the model draws on patterns in its parametric memory and can invent details — a wrong figure, a misattributed quote, a product feature that does not exist — stated with full confidence.
For brands this cuts both ways. An engine may misdescribe your product, attribute a competitor's strength to you, or omit you entirely while confidently naming someone else. Web-grounding, retrieval and clear authoritative content reduce the gaps a model would otherwise fill by guessing.
This is a core reason AEO and GEO matter: well-structured, citable, entity-clear content gives engines something correct to ground on. SkuLift surfaces how a brand is described across engines so misstatements can be caught and corrected at the source.