In documentation contexts, hallucinations look like plausible answers about your product that are simply wrong: an invented CLI flag, a deprecated endpoint presented as current, or a code example that never worked. A plausible answer can be difficult for readers to distinguish from a correct one without checking the source.
Hallucinations happen because language models generate likely-sounding text rather than looking facts up. Without access to your docs, a model asked about your product will interpolate from similar products in its training data.
The main defenses are retrieval and refusal. A RAG system grounds answers in retrieved docs passages, and a well-configured assistant declines to answer when nothing relevant is found rather than filling the gap with a guess.