title: "Génération augmentée par la recherche (RAG) : libérer le potentiel de l’IA générative"

Retrieval-Augmented Generation (RAG) is a technique that enhances generative AI for enterprise use by grounding models in specific, external information. It combines a retrieval component, which fetches relevant data from a knowledge base, with the generative capabilities of a large language model. This approach allows the AI to produce more accurate, reliable, and contextually relevant responses, overcoming the limitations of static training data. RAG is presented as a key method for operationalizing generative AI, revolutionizing processes for information retrieval and content creation within a business context.