Retrieval-Augmented Generation (RAG)

A technique that combines a language model with a search system. When a user asks a question, the system first retrieves relevant documents from a database, then feeds those documents to the language model to generate an answer grounded in the retrieved content. RAG reduces hallucinations and allows AI systems to work with private or up-to-date information without retraining the model. From a governance perspective, RAG systems require clear data access policies — the retrieval database determines what information the AI can see and use.

A company deploys a RAG system that lets employees query their internal HR policy documents using natural language. The AI can answer questions accurately because it retrieves relevant policy sections before generating a response.