Retrieval-augmented generation (RAG)

Retrieval-augmented generation is the standard way to make an AI answer from your information instead of its general training. When someone asks a question, the system first searches your documents for the most relevant passages, hands those passages to the language model, and tells it to answer using only what it was given. The answer can then cite which document it came from.

The reason RAG matters: it's the difference between an AI that vaguely remembers how warranties usually work and one that quotes your warranty policy. It also means updating the AI is as simple as updating the document. No retraining, no waiting.

Practical example: a property management company with hundreds of lease agreements can ask 'which tenants have early-termination clauses?' and get an answer with the specific leases cited. That's RAG doing the unglamorous work: search first, then write. Nearly every document AI and knowledge assistant we build uses it.