Key terms in two registers: a plain-language definition for language professionals and a technical one for IT/ML readers.
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Tying a model's answer to a trusted source — documents, a database, search results — so it reports what the source says instead of what merely sounds plausible. The main defence against made-up answers.
Conditioning generation on retrieved, authoritative context (documents, tools, knowledge bases) so outputs are attributable to a source. Underlies RAG and citation systems; reduces but does not eliminate hallucination.
When a model states something false as if it were true — a made-up fact, citation, or quote — because it predicts fluent text, not verified truth. Confident wording is no guarantee of accuracy.
Generation of content unsupported by the input or by fact, arising because a language model optimises next-token likelihood rather than truth. Mitigated (not solved) by grounding/RAG, retrieval, verification, and calibration; intrinsic vs extrinsic types.