Definition
A technique that connects an AI model to the company's documents: the AI retrieves the relevant passages before answering, with cited sources.
RAG (Retrieval-Augmented Generation) solves the main limitation of LLMs: they don't know your internal data. The principle: your documents (contracts, procedures, wikis, emails) are indexed in a vector database; for each question, the system retrieves the most relevant passages and provides them to the model, which answers while citing its sources.
RAG sharply reduces hallucinations and makes AI genuinely useful in the enterprise. Sovereign point of attention: the index contains a condensed copy of your most sensitive documents, it must be hosted within your perimeter, not in a third party's cloud.
An accounting firm connects its assistant to its 4,000 client files. A staff member asks for a company's year-end date: the assistant finds the answer in the right file and shows the source document.
As soon as answers must come from your own documents: internal procedures, contracts, knowledge base, client history.
Does RAG send my documents outside?
No, if it is installed within your perimeter. The documents and their index stay on your servers or with a sovereign host, and the model runs in the same place.
All AI models, deployed on your infrastructure, with your documents connected.