What Is RAG (Retrieval-Augmented Generation)? Simple Explanation
RAG (retrieval-augmented generation): Retrieval-augmented generation (RAG) is a technique in which an AI system first searches a specific set of documents, such as your policies, FAQs or contracts, for relevant passages, and then writes its answer using only those passages, ideally with citations.
Why it matters
A language model on its own answers from what it learned during training. That knowledge may be outdated and knows nothing about your business. RAG gives it the right pages from your documents at the moment of answering. That makes answers more accurate, keeps them current, and lets users check the source.
Business uses
- Customer chatbots that answer from your FAQs and policies.
- Internal assistants that search SOPs, templates and past work.
- Legal and compliance tools that find relevant clauses with citations.
RAG reduces, but doesn’t eliminate, wrong answers. Good systems still show sources and hand uncertain questions to a person. See our custom AI apps service.
Related terms: Large language model (LLM) · AI chatbot · AI agent