{
  "dataset": "glossary",
  "record": {
    "id": "hallucination",
    "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": {
      "value": null,
      "verify_status": "verify-against-primary-at-build",
      "source_hint": "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"
  }
}