What is RAG – how to make a chatbot answer correctly from your company's documents
An AI model doesn't know your company's price list or return policy. RAG (Retrieval-Augmented Generation) is a way to give the AI that information exactly when it needs it.
How RAG works
- Preparation. Your company's documents are split into small passages and stored in an index that can be searched by meaning.
- Retrieval. When a question comes in, the system finds the most relevant passages.
- Answer. The AI receives the question together with those passages and writes an answer based on them.
Why businesses often use RAG
- Updating information only means editing the documents, not retraining the model.
- The source of each answer can be shown so users can check it.
- Reduces the AI guessing when it has no information.
What RAG does not solve
RAG reduces wrong answers but does not eliminate them. If a document is outdated or contradictory, the AI will answer according to that wrong document. Document quality determines answer quality.
What to prepare
- The documents are readable text, and someone is responsible for keeping them updated.
- A set of real customer questions for testing before go-live.
- Define what the chatbot may answer and what must be passed to staff.
Read next: AI customer service chatbots – when to build one.
