Retrieval-Augmented Generation (RAG) is a technique in natural language processing that combines two core components: a retrieval system to fetch relevant information from a database or document collection and a generative model (e.g., a large language model) to process this information and produce contextually appropriate outputs.
This a method where a system looks up relevant information from a database or documents and uses that information to generate accurate and useful responses. Instead of guessing or relying only on what it knows, RAG finds the right facts first and then combines them with its language skills to create better answers. This helps avoid mistakes and ensures the response is based on real, up-to-date information.