How retrieval-augmented generation works
The approach was named in a 2020 paper by Lewis and colleagues, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, which paired a language model with a retriever over an indexed document collection. Business assistants follow the same pattern: documents are split into passages and indexed, the question is matched against them, and the best passages go to the model with instructions to answer from them.
RAG reduces made-up answers but doesn’t eliminate them. If retrieval misses the right passage, or two sources disagree, the model can still produce a wrong answer. That’s why good systems also tell the model to admit when the sources don’t cover a question.
Example: a therapy practice’s cancellation policy
A therapy practice uploads its intake packet, which includes a 24-hour cancellation policy and fee schedule. When a client asks by chat what happens if they cancel the morning of a session, the assistant retrieves the policy passage and quotes the fee correctly instead of guessing. No client health information is involved in answering this. This scenario is illustrative.
RAG in InsertChat
InsertChat answers from your knowledge base using retrieval: your website pages, documents, spreadsheets, and written answers are processed and searched before each reply on every channel. You can let the assistant mention where an answer came from when helpful, and it says when the information isn’t available.
Check a source’s processing status before relying on it, and refresh it when your policies change. Practices that need to handle protected health information require Enterprise and a signed business associate agreement. See the therapy practice setup.
