AI Hallucination
Inaccurate or fabricated output presented as fact.
Technical Term
Full explanation
An AI hallucination is inaccurate or fabricated output produced by an AI system and presented as though it were fact.
It happens because a language model is, at its core, predicting plausible next words based on patterns in its training data, not looking facts up in a verified database. When the model has thin, conflicting, or absent information about something, it can still produce a fluent, confident-sounding answer that is simply wrong, because fluency and accuracy are not the same capability.
This is a general property of how these systems work, not a bug specific to any one brand or topic. What a brand can do about it is indirect: increase the quantity and consistency of accurate, well-corroborated information available for the model to draw on, which lowers the odds of a gap the model fills in by guessing.
Why this matters
It is the general technical failure. Brand Hallucination is the specific commercial version, and the difference matters when explaining what a brand can actually do about it.
Example
Asked a detailed question about a small company with a thin public record, a model might generate a confident-sounding but invented answer, such as a founding date or a product that does not exist, because it has almost nothing accurate to draw on and fills the gap with a plausible-sounding guess. That is an AI hallucination; when the invented detail is specifically about a brand, it becomes a Brand Hallucination.
Where this sits
AI Hallucination belongs to AI & Search Infrastructure, one of the seven layers of the Aingworth Glossary. The technical vocabulary behind the interfaces. Useful for understanding how the systems work, without becoming the center of the conversation.
Related terms
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