Ask a general AI model about your return policy and it will invent a plausible answer. Ask an AI connected to your documents and it will quote the policy you actually wrote. The technique behind this is called retrieval-augmented generation, or RAG.
How RAG works
Your documents are split into passages and indexed. When someone asks a question, the system first looks for the most relevant passages, then hands them to the model along with the question. The model writes its answer from these passages and can cite them.
Where it makes a difference
- 01Internal assistants that answer questions about procedures and policies
- 02Customer support based on your help center and product documentation
- 03Sales teams looking for the right reference or technical detail
- 04Legal and compliance teams searching contracts and regulations
What makes a RAG system reliable
Answer quality depends far more on the data than on the model. Outdated documents, duplicates and badly scanned PDFs lead to wrong answers. A good system keeps its sources in sync, respects access rights and shows where each answer comes from.
Measure before you trust
Build a set of real questions with their expected answers, and test the system against it before launch and after every change. That is how you know the accuracy instead of guessing it.
Thinking of an assistant that knows your company’s documents? We can assess your sources and show you what is possible in a free audit.