This Atos white paper presents a practical blueprint for implementing Retrieval-Augmented Generation (RAG), a GenAI technique that enhances LLMs by integrating external knowledge sources. RAG improves accuracy, personalization, and real-time insights across industries like customer service, legal, and R\&D. It combines information retrieval with natural language generation and requires strong data, security, and governance practices. The paper outlines implementation best practices using MLOps and showcases Azure as a case study. Future RAG trends include multimodal data, quantum computing, and ethical AI innovations.

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