Hallucination

A model produces text that is statistically plausible, not text that has been checked. When the facts are missing (never in its training data, too recent or too obscure), it can fill the gap with something that sounds right: a product feature that does not exist, a court case that was never heard, a link that leads nowhere.

The risk can be reduced but not removed. Ground answers in your own documents (RAG) and ask for citations, allow the model to say it does not know, validate structured output, let tools do arithmetic and lookups, and keep a person in the loop for anything legal, medical or financial. Treat model output as a draft to check, not as a source.