What is an AI Hallucination?

Hallucination happens because a language model is fundamentally predicting plausible next tokens based on patterns in its training data, not looking up verified facts in a database. When it lacks solid grounding for a specific claim, it doesn't reliably know that it doesn't know, it still generates fluent, confident-sounding text, because fluency and confidence are what its training optimized for, not calibrated uncertainty. Mitigation strategies work by giving the model something real to ground its answer in rather than relying on parametric memory alone: retrieval-augmented generation supplies verified source documents at query time, tool use lets the model check a live API instead of guessing, and structured verification steps have a second pass fact-check claims before they reach the user. None of these eliminate hallucination entirely, so systems handling consequential decisions still pair AI output with human review or automated verification against a ground truth.