Hallucination

When a language model generates fluent output that is false or unsupported by its sources, presented as if it were fact.

Hallucination is why the agentic web values grounding, structured data and verifiable sources: an agent that can retrieve a fact does not have to invent one.

term
Hallucination
category
core
short_def
When a language model generates fluent output that is false or unsupported by its sources, presented as if it were fact.
long_def
A hallucination is a confident but ungrounded generation: the model predicts plausible tokens rather than retrieving a verified fact, so the answer can be wholly invented — a citation, a number, an API that does not exist. It is a property of next-token prediction, not a bug, which is why grounding and retrieval are used to constrain it.
see_also
grounding rag ai-agent
etymology_origin
— verify-against-primary-at-build ↗ https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence) — term borrowed from human perception; adopted for generative models in the late 2010s, no single coiner
related_to
grounding rag agentic-rag ai-agent
contrast_with
Unlike a factual error copied from a bad source, a hallucination is generated with no source at all — the model fabricates it from statistical likelihood.
example
An agent asked for a library's API might invent a method that reads plausibly but does not exist; grounding the agent in the real docs is what prevents it.
source
https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence)
status
active
why_it_matters
Hallucination is why the agentic web values grounding, structured data and verifiable sources: an agent that can retrieve a fact does not have to invent one.
sameAs
https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence)
bridge_entity
grounding
last_verified
2026-07-06
md_twin
/glossary/hallucination.md

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