What Is RAG and Why Is It Important for AI Applications?

Learn how Retrieval-Augmented Generation allows AI applications to work with private and up-to-date information.

Retrieval-Augmented Generation, commonly called RAG, is a technique that allows an AI model to retrieve information from an external knowledge source before generating an answer. Instead of relying only on information learned during model training, a RAG system can search documents, databases, websites, or other knowledge sources. Documents are processed into chunks, embeddings are generated, and the embeddings are stored in a vector database. When a user asks a question, relevant information is retrieved and provided to the language model to generate the response.