What is an AI hallucination?

Why it matters

This is the core trust risk in any AI product. An LLM does not know what is true; it predicts text that reads as likely given the prompt, and sometimes the likeliest-sounding text is simply wrong. The danger is the confidence: a hallucination looks exactly like a correct answer, so users cannot tell the difference on their own. Ship a feature that invents policy details or fake numbers in front of a customer and you lose trust fast, which is why this has to be designed for from day one, not patched later.

How to reduce it

You cannot fully eliminate hallucination, but you can push it down a lot. Grounding the model with RAG so it answers from real, retrieved sources is the biggest lever, because the model has actual facts in front of it instead of guessing. On top of that, validate outputs against known data, constrain the format so answers are checkable, and run evals to catch regressions before users do. The goal is to make wrong answers rare and, when they happen, easy to catch.

A concrete example

Ask a bare LLM for the price of a specific plan and it may state a clean, confident figure that was never real. Wire the same question to your live pricing data through RAG, and the model answers from the actual source, or admits it does not have it. That gap is the whole reason we ground AI features in customer products rather than letting the model speak from memory.