Knowledge RAG: Bridging Semantic Gaps with Hybrid Retrieval and Query Rewriting

Analyze Happy: Crafting Your Modern Data Estate

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The challenge of semantic gaps in RAG systems, where embeddings often miss crucial connections—like linking a "PTO" query to policy language on "prorated annual leave". It details Knowledge RAG design principles for higher accuracy and user satisfaction, emphasizing proven 2025 techniques. These techniques include using LLMs for query rephrasing and multi-query generation, hybrid retrieval fusion that blends semantic vectors with keyword search, and reranking results to ensure top-k precision. The goal is to design pipelines that account for semantic mismatch, rather than expecting embeddings to "understand" complex intent.

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