Hallucination

A hallucination is a language model answer that sounds linguistically flawless and self-assured but is factually wrong or entirely made up. It happens because the model does not query a database - it generates the statistically most likely next word. Plausibility is the objective; truth is merely a frequent side effect. When the model lacks the information, it fills the gap with something that sounds right.

Hallucinations are dangerous precisely because they arrive without a warning sign: invented figures, quotes, legal clauses or contact names appear in the same assured tone as verified facts. In everyday marketing terms - an AI text cites a study that does not exist, a chatbot promises a feature that was never built, an agent emails the contact who left the company long ago.

Hallucinations never disappear entirely, but they can be contained systematically: retrieval-augmented generation forces the model to answer from real documents, a clear system prompt establishes that missing knowledge is named rather than invented, and human-in-the-loop ensures a person reviews critical content before it ships. The foundation remains clean data - on a corrupted database, even the best setup will merely quote the error with precision.