Why LLMs Hallucinate (and How to Reduce It)
LLM hallucinations — confident, fluent answers that happen to be false — are the number-one reason teams get burned when they ship AI features. This guide is for builders who want to understand why models make things up and, more importantly, what actually reduces it. We will reframe hallucination as a predictable behavior, then work through grounding, prompting, and verification tactics that hold up in practice. In this guide What 'hallucination' means here Why it happens: prediction, not lookup When models are most likely to make things up Grounding with retrieval Asking models to say 'I don't know' Verification patterns What you can't fully fix (yet) FAQ Quick answer: Hallucinations happen because a model predicts plausible text rather than looking facts up, so it fills gaps with confident guesses. You cannot fully eliminate this, but grounding the model in real sources, letting it say “I do not know,” and verifying key claims cut it down sha...