Quick summary: RAG (Retrieval-Augmented Generation) lets AI answer from your real documents instead of memorised training data, in two steps: retrieving relevant passages, then generating an answer with citations to verify.

RAG — Retrieval-Augmented Generation — is the foundational technique that lets an AI assistant answer from your company's own knowledge rather than only from what the model memorised during training. It is the single most important piece for turning a general language model into an expert on your business.

A language model on its own, however powerful, has two inherent limits: it does not know internal documents it was never trained on, and it can "hallucinate" plausible-sounding but wrong information. RAG is designed to fix both.

The mechanism has two stages. In the retrieval stage, when a user asks a question, the system searches a vector database for the passages most semantically relevant. In the generation stage, those passages are fed alongside the question so the model composes an answer — but only from the evidence just retrieved.

The biggest benefit is verifiability. Every answer comes with citations to the source documents, letting users click through and confirm. In fields like law, finance or healthcare, this ability to "point to the evidence" is not a nice extra but a prerequisite for AI to be trusted.

RAG is also flexible and economical. When documents change, you simply update the knowledge base — no expensive retraining needed. The assistant always answers from the latest version, and you control precisely which sources may be used.

Combine RAG with a Local AI architecture and you get the best of both worlds: accurate, clearly sourced answers grounded in your entire organisational knowledge — without a single byte of data leaving your infrastructure.

Frequently asked questions

What is RAG? A technique combining document retrieval with a language model's generation ability, keeping answers grounded in real documents.

What problem does RAG solve? It fixes the model not knowing internal documents it was never trained on, and sharply reduces the risk of hallucinated information.

Do we need to retrain the model when documents change? No — you simply update the knowledge base; no expensive retraining is needed.