{"id":1267,"date":"2026-03-04T10:07:40","date_gmt":"2026-03-04T10:07:40","guid":{"rendered":"https:\/\/ai-enterprise.fr\/?p=1267"},"modified":"2026-03-04T12:40:42","modified_gmt":"2026-03-04T12:40:42","slug":"rag-entreprise-documents-internes-intelligence-artificielle","status":"publish","type":"post","link":"https:\/\/ai-enterprise.fr\/en\/rag-entreprise-documents-internes-intelligence-artificielle\/","title":{"rendered":"RAG in business: leverage your internal documents with artificial intelligence"},"content":{"rendered":"
\"Contenu<\/figure>\n\n\n\n

One of the main challenges of generative AI in business is the reliability of its responses. How can we ensure that an AI agent isn't "hallucinating," that it's relying on verified and up-to-date data? The answer lies in three letters: RAG<\/strong> (Retrieval-Augmented Generation).<\/p>\n\n\n\n

RAG: what is it and why is it essential?<\/h2>\n\n\n\n

RAG is an architecture that combines the power of Language Learning Models (LLMs) with information retrieval from a document database. Specifically, before generating a response, the AI agent will first search for relevant information in your internal documents<\/strong>, then use it to construct a precise, sourced and coherent answer.<\/p>\n\n\n\n

This is the fundamental difference between a "naked" LLM \u2014 which only knows its training data \u2014 and a AI agent augmented by your company data<\/strong>.<\/p>\n\n\n\n

What internal documents can be used to populate the RAG?<\/h2>\n\n\n\n

On a platform like AI-Enterprise, you can connect a wide variety of sources:<\/p>\n\n\n\n