How Hybrid Retrieval and Reranking Take RAG Pipelines to Production Grade

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Basic Retrieval-Augmented Generation (RAG) setups commonly used in tutorials fall short when deployed in real production environments. Hybrid retrieval, which combines dense and sparse search methods, addresses the limitations of standard vector-only retrieval. Adding a reranker further refines results by scoring retrieved chunks for relevance before passing them to the language model. LangChain serves as the orchestration framework for assembling these advanced pipeline components. Measuring performance at each stage is emphasized as essential for maintaining reliability and quality in production systems.

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