Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation, commonly known as RAG, is a technique that grounds AI responses in your organization's own documents and data rather than relying solely on the AI model's general training. A RAG system operates in three stages: documents are processed into embeddings in a vector database, semantic search finds relevant passages, and the AI generates a response grounded in those sources. For regulated industries, RAG is particularly valuable because it enables AI capabilities while maintaining data sovereignty.