Generic AI chatbots don't know your products, policies or customers. Retrieval-augmented generation — RAG — fixes that by letting the model look up your own information before it answers. Here is how it works and what makes it reliable.
How RAG works
- Index — your documents are split into passages and stored in a search index that understands meaning, not just keywords.
- Retrieve — when a user asks a question, the most relevant passages are found.
- Generate — the language model writes an answer using only those passages, and cites them.
What makes it reliable
- Good source content — the assistant can only be as accurate as the documents it reads.
- Citations — every answer should link to where it came from, so users can verify it.
- Evaluation — a set of real questions with known answers, tested on every change.
- Graceful hand-off — when confidence is low, route the conversation to a human.
Our article on shipping AI features users actually trust covers these habits in more depth.
Keeping data private
Choose providers that don't train on your data, restrict what each user can retrieve based on their permissions, and consider hosting inside your own cloud account for sensitive information.
Where to start
Pick one high-volume use case — such as support questions or internal policy lookups — and measure how many questions the assistant answers correctly before expanding.
Our AI integration services team designs, builds and evaluates assistants grounded in your data.