title: "RAG pour les LLMs : une IA plus intelligente avec la génération augmentée par récupération"

Retrieval-Augmented Generation (RAG) is a technique for enhancing Large Language Models (LLMs) by providing them with real-time, external knowledge. This method improves the accuracy, reliability, and performance of AI within enterprise teams. By connecting LLMs to current information sources, RAG allows them to generate more trustworthy and precise outputs. A key advantage is that these improvements are achieved without the need to completely retrain the underlying model.