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Retrieval-Augmented Generation (RAG) method are transforming the landscape of natural language processing by combining the strengths of retrieval-based and generative models (LLMs). When deployed in production, RAG systems can provide more accurate and contextually relevant responses. This guide outlines best practices for implementing RAG models in a production environment, ensuring robustness, scalability, and efficiency.