RAG – Retrieval-Augmented Generation

RAG (Retrieval-Augmented Generation) is a technique in which a language model first searches for relevant information in an external database, documents, or the web before generating a response, and bases its response on the facts it finds. This approach significantly reduces the risk of hallucinations and allows the model to work with up-to-date information or internal company data that was not part of its original training. RAG is now the standard architecture for building enterprise AI chatbots and assistants that operate using their own knowledge base.