Retrieval-Augmented Generation (RAG)
An approach in which an AI model first retrieves relevant facts from a knowledge source and uses them to formulate a current and accurate answer.
By Tanguy De Keyzer · Founder & digital strategist
Retrieval-Augmented Generation (RAG) is a technique in which a large language model first retrieves facts from an external knowledge source and only then generates an answer. Instead of relying solely on what it learned during training, the model searches through relevant documents and bases its answer on that current source. This keeps it accurate and up to date.
How does it work?
RAG combines two steps. First, the system searches a knowledge base, for example your product documentation, knowledge base articles or CRM data, for the passages most relevant to the question. Then the language model receives those found passages in its prompt and formulates an answer based on them. The model therefore invents less, because it can hold on to real sources instead of guessing from its memory.
Why it matters for B2B
For a B2B organisation, the value lies in reliability. A standard language model knows nothing about your prices, your cases or your internal processes, and can confidently spout nonsense about them. With RAG you link the model to your own, correct information. That makes it usable for an internal support assistant, a quotation helper or a chatbot that answers customers correctly. Well-fed RAG systems also cite their sources, which increases trust and can support your citation rate in AI answers.
The pitfall
RAG is only as good as the knowledge source underneath it. Feed it outdated, contradictory or messy documents, and the model retrieves messy facts and the answer still sounds convincingly wrong. The gain therefore does not lie in the technique alone, but in the maintenance of your knowledge base. Build RAG pragmatically around a goal that truly matters, such as less support work or faster quotations, and not to fill a dashboard with AI features.
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