Core Concepts

What Is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation (RAG) is a technique that pairs a language model with a retrieval step: relevant documents are fetched from a knowledge source and supplied to the model as context before it answers. This grounds responses in specific, up-to-date information and reduces reliance on the model’s fixed training data, helping limit hallucinations.