This whitepaper explores Retrieval-Augmented Generation (RAG) as a key technique for enhancing the trustworthiness, controllability, and efficiency of large language model (LLM) applications in industry. It highlights strategies for industrializing RAG, advanced techniques like HyDE and metadata filtering, and recipes for solving challenges such as cold starts and multimodal reasoning. Evaluation metrics for RAG systems are detailed, emphasizing context relevance, recall, and precision. The paper underscores the need for modular, scalable RAG systems and future opportunities in integrating reasoning, agents, and cross-modal data.

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