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RAG Explained: How Retrieval Augmented Generation Actually Works

Retrieval-augmented generation (RAG), introduced in early 2021, augments large language models by letting them retrieve relevant information from external data stores before generating answers, overcoming the limits of small context windows. RAG workflows convert documents into vector embeddings using models like OpenAI’s text-embedding-3 or Cohere, store them in vector databases such as Chroma or Pinecone, and query those vectors to provide semantically relevant context to LLMs. Since its introduction, RAG has matured into a standard architecture for grounding model output in external knowledge and supporting broader, domain-specific use cases. The approach is now considered a core capability for building reliable, up-to-date AI systems.