AI & automation
RAG (Retrieval-Augmented Generation)
RAG is a technique that grounds an AI model's answers in your own documents, so it responds from real, retrievable facts instead of guessing.
Retrieval-Augmented Generation (RAG) is a technique that connects a large language model to an external knowledge source — your documents, policies, product data — at answer time. The system retrieves the most relevant passages and feeds them to the model as context, so the answer is grounded in your real content.
RAG is how a trustworthy AI chatbot or internal copilot is built: it dramatically reduces hallucination, lets answers cite sources, and means the assistant stays current as your documents change — without retraining the model.