An AI chatbot such as ChatGPT or Claude knows a lot, but nothing about your business. It does not know your manuals, your price agreements or your contracts. Still, you can make AI work with your own knowledge. The technique behind it is called RAG: Retrieval-Augmented Generation. In this article we explain what it is, when it works and what to watch out for.
How does RAG work?
RAG works in two steps.
- Search. When someone asks a question, the system first searches your own documents for the pieces of text that best match the question. These can be manuals, contracts, knowledge base articles or emails.
- Answer. Then the AI model gets the question together with those pieces. The model writes an answer based on your information, and names the source.
So the model is not trained on your data. It reads the relevant pieces again for every question. That keeps the answers current: a new version of a document is usable straight away.
When does RAG make sense?
RAG works well when a lot of knowledge sits in documents and people often have questions about it. For example:
- Customer service: an assistant that answers customer questions from your manuals and terms.
- Internal knowledge: staff who want to find a procedure, a price agreement or a technical specification quickly.
- Document processing: searching contracts or quotes for agreements, deadlines or risks.
- Onboarding: new colleagues who find answers themselves, without interrupting a colleague every time.
When does it work less well?
RAG is not a solution for everything. It works less well when:
- the information is not written down, but lives only in people’s heads;
- documents contradict each other or are out of date;
- the answer needs a calculation over a lot of data, such as revenue per customer. A connection to your database or a dashboard is often better for that.
What should you watch out for?
The quality of your documents. An AI assistant is only as good as the information it finds. Clean up outdated material and make sure there is one current version.
Sources. Let the system always show the source. Then the user can check the answer, and you quickly see which document needs work.
Privacy and security. Business data is sensitive. Choose a setup where your data is stored securely, for example in the Netherlands or in your own environment, and where the AI provider does not use your data to train models. Also decide who may see which documents.
Start small. Begin with one clear use, such as questions about your manuals. Measure whether the answers are correct and whether people use it. Then expand.
Do it yourself or have it built?
For a first experiment you can do a lot with existing tools. Our Do It Yourself page lists tools such as Claude that you can start with. Do you want to connect AI to your own systems, with proper security and rights for each user? Then we are happy to build it for you. Read more about our AI integrations or book a free consultation.