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A production RAG workflow requires selecting the right embedding model, configuring vector database indexing and chunking strategies, and implementing hybrid search that combines semantic vector search with keyword fallback to maximize retrieval quality. RAG evaluation must measure retrieval precision and generation faithfulness independently, because strong LLM performance cannot compensate for a weak information retrieval component, and continuous data updates are essential to prevent stale knowledge from degrading response accuracy. Retrieval Augmented Generation (RAG) is an AI architecture pattern that connects large language models to external knowledge sources at inference time, enabling those models to generate accurate, context-aware responses that go beyond their static training data. 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