What is Retrieval-Augmented Generation (RAG) and how does it work?

Retrieval-Augmented Generation, or RAG, is a technique that combines a retrieval system with a language model to ground responses in external knowledge. The system first retrieves relevant documents from a knowledge base using semantic search, then passes those documents as context to the language model along with the user query. The model generates an answer based on the retrieved context rather than relying solely on its parametric memory. This reduces hallucinations and lets the model use up-to-date or proprietary information without retraining.