Retrieval-augmented generation in LLMs
How RAG extends an LLM's knowledge with a company's internal documents, without retraining the model, and what it looks like when it actually works in production.
LLMs are impressive until you need them to know something about your organisation. They have no access to internal documents, and a model that always wants to give an answer will hallucinate one rather than admit ignorance. Retrieval-Augmented Generation solves this by connecting the model to a searchable knowledge base at query time, without retraining.
The article explains the four-step pipeline: chunking and embedding documents, embedding the user's question, retrieving the closest matches, and generating a grounded answer. It covers practical business applications and the limits: what happens when retrieval misses the user's intent, and how citations close the remaining gap.
Originally published in Dutch on the Future Facts Conclusion blog, March 2024.