How retrieval-augmented generation works, when it's the right tool, and what it takes to run it well.
Retrieval-augmented generation, or RAG, lets an AI answer questions using your own documents instead of only what it learned in training. It's how docs copilots and support assistants stay accurate.
How it works
Your documents are split into small passages and indexed. When someone asks a question, the system finds the most relevant passages and gives them to the model, which writes an answer and cites its sources.
What makes it work well
- Clean, up-to-date source documents
- Search tuned to how your users actually ask
- Answers that show their sources
- An eval set to catch regressions
For one developer-tools client, a docs copilot built this way cut support tickets by almost a third.