Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines a language model with a retrieval system — fetching relevant documents at query time and feeding them to the model as context. It reduces hallucination and enables AI to reason over private or up-to-date knowledge.
A SaaS help center uses RAG: when a user asks a question, the system retrieves the 5 most relevant help docs and feeds them to an LLM, which generates a precise, grounded answer.