If you have ever asked an AI chatbot something about your company, you know the problem: it doesn’t know your products, your prices or your procedures. And when it doesn’t know something, it sometimes makes it up with total confidence.
RAG (Retrieval-Augmented Generation) is the technique that fixes that. Instead of relying on what the model “remembers”, the system first finds the relevant passages in your documents and then asks the model to answer using only that information.
How it works, in three steps
- Prepare the documents. Manuals, spec sheets, FAQs or policies are collected, split into passages and stored in an index that searches by meaning, not just exact words. A question about “sending an order back” finds the “returns and refunds” section.
- Search. When someone asks a question, the system looks up the most similar passages in that index.
- Answer. The question and those passages go to the language model, with instructions to answer from them and say which document each fact came from.
The result is like having a colleague who has read all the documentation and answers you with the page number.
Advantages over a generic chatbot
- It answers with your information, not with whatever is on the internet.
- It cites its sources, so every answer can be checked.
- It is easy to update. Change a price or a procedure in the document and you’re done; no model retraining.
- You control what it knows. It only uses the documents you include, and access can be limited per user.
Where it works very well
- Technical support: technicians ask about an error or a part and get the exact section of the manual.
- Sales teams: quick answers on features, compatibility or terms without calling the office.
- Onboarding: new staff answer their own questions about procedures without interrupting anyone.
- Customer service: a website assistant that answers with your real shipping, warranty and returns terms.
- Law firms and consultancies: searching regulations, contracts or past cases.
When it is not the best option
- If you have little documentation, or it is badly out of date. RAG is only as good as the documents you give it.
- If you need exact calculations over structured data (sales, stock). It is better to query the database directly; AI can help write that query.
- If answers change by the minute, like live prices. Then it is better to connect the AI to your system rather than to documents.
What to watch out for
- Document quality. Tidying up the documentation before you start usually has the biggest impact on results.
- Permissions. If some documents are confidential, the system must respect who can see what.
- Where data is processed. Pick providers and settings that comply with data protection rules, especially with personal information.
- Measure. Keep a list of real questions with their correct answers and check regularly that the system still gets them right.
A RAG search over a company’s documentation is one of the AI projects that saves the most time. Want to know if it makes sense for you? See what we do with AI or get in touch. If you are just getting started, you may want to read 7 AI uses for small businesses first.