Hallucination
A hallucination is a confident but false statement produced by an AI model, such as a chatbot inventing a configuration option that does not exist in the documentation.
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.
Frequently asked questions
Can hallucinations be fully eliminated?
No. Grounding, source links, and declining unsupported questions can reduce errors. Readers should still check important answers against the docs.
Why do chatbots hallucinate about products?
A model may rely on outdated training data or confuse your product with similar ones. Retrieving your documentation gives it a more relevant source, though answers can still be wrong.