RAG – what is it?

Retrieval-Augmented Generation (RAG) is an architectural approach in artificial intelligence systems that combines generative models with mechanisms for retrieving relevant information from external data sources. In this paradigm, RAG assumes that before generating a response, the model is provided with additional context retrieved from knowledge bases, documents, or indexes built using vector search or other information retrieval methods. This reduces dependence on knowledge embedded during model training and improves response accuracy by incorporating up-to-date data.

In practical implementations, RAG is used to build enterprise search systems, chatbots, analytical tools, and document-centric services. The architecture includes components for data indexing, creation of vector representations (embeddings), retrieval mechanisms, and integration with a language model for response generation. This approach enables the use of internal organizational knowledge, ensures control over information sources, and reduces the risk of generating incorrect or hallucinated outputs in production AI systems.