Knowledge base and RAG

RAG stands for retrieval-augmented generation. Instead of relying on the general knowledge of the language model, the system looks up the matching passages in the stored documents for every question and gives them to the model as a basis. That way the assistant answers with the information of the business and not with guesses.

Quality depends on the content. Short, unambiguous entries work well: opening hours, services, prices, directions, procedures. Outdated pages, contradictory details and long running text work badly. It matters who maintains the content and how changes are approved. With small amounts of knowledge, the entire content can also sit directly in the prompt; retrieval only pays off at a larger scale.