A hallucination is when an AI model generates output that is fluent and confident but factually wrong or unsupported. It's the core reliability risk of LLMs, mitigated with retrieval grounding, citations and guardrails.
Because LLMs predict likely text rather than look up facts, they can invent details. Grounding answers in retrieved sources (RAG), requiring citations, and constraining scope are the standard defences.
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