An AI architecture that grounds language model responses in retrieved, factual documents.
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances language model responses by first retrieving relevant documents from an external knowledge base, then using those documents as context for generating a response. Rather than relying solely on knowledge baked into the model's parameters during training, RAG allows AI systems to access up-to-date, domain-specific, and proprietary information at query time.
RAG has become the dominant architecture for enterprise AI deployment. It solves the two most critical problems with pure language models: hallucination (making up facts) and knowledge cutoff (not knowing recent events). In 2026, virtually every enterprise AI assistant uses some form of RAG. The quality of the retrieval step — which depends heavily on semantic search and embedding quality — determines the overall system quality.
A RAG system has three main components: an indexing pipeline (which chunks documents, generates embeddings, and stores them in a vector database), a retrieval engine (which takes a user query, embeds it, and finds the most similar document chunks), and a generation model (which takes the retrieved chunks as context and generates a response). Advanced RAG systems add re-ranking, query expansion, and hybrid search to improve retrieval quality.
A law firm deploys a RAG system over their 50,000-document case library. When a lawyer asks 'What precedents exist for software patent infringement in the Northern District of California?', the system retrieves the 10 most semantically relevant case documents and provides a synthesized answer with citations — rather than relying on the model's general legal training.