A hallucination is when a model states something false with total confidence. It isn’t lying, it has no notion of truth. An LLM is trained to produce plausible next tokens, and a fluent falsehood is often more plausible-sounding than an honest “I don’t know.”
Hallucinations rise when the model is asked about things outside its training data, when the prompt is ambiguous, or when temperature is high. The main mitigations: ground the model in real sources via RAG, ask for citations, and lower temperature for factual tasks.