RAG stands for retrieval-augmented generation. Behind that technical term sits a simple, powerful idea: an AI that does not answer from memory, but first looks up the right passage in your own documents and bases its answer on that. Without the jargon: a system that answers only from your own sources, and always shows where the answer came from.
How it works, in plain language
When someone asks a question, at its core this happens:
- The system searches your documents for the most relevant passages.
- It gives those passages, together with the question, to the language model.
- The model formulates an answer based on those passages, with a reference to the source.
Is the answer not in your documents? Then a well-built RAG chatbot should say so honestly, instead of inventing something plausible.
Why source citations make the difference
A language model can always produce a fluent, convincing answer, even when it is wrong. We call that a hallucination, and it is the biggest risk of AI. Source citations are the antidote: with every answer the user sees exactly which document it is based on and can verify it themselves. That makes AI checkable instead of a black box.
For domains where mistakes are not an option, this is not a luxury but a requirement. We proved it with KiesKennis, the election chatbot for eight regional broadcasters: 43,943 questions answered, 99% factually accurate, every answer with a clickable source. More on how we think about reliability in ChatGPT or a custom AI solution.
Where RAG delivers value for your organisation
Anywhere people need fast, reliable answers from a lot of information: an internal knowledge assistant for employees, customer service based on your own knowledge base, or searching through contracts and policy. It is the same technology, each time applied to your own sources.
Curious what a RAG solution could mean for your organisation? Take the free AI scan or book a no-obligation intro call.