Hybrid RAG Architectures: Combining BM25 with Dense Semantic Embeddings
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Hybrid RAG Architectures: Combining BM25 with Dense Semantic Embeddings

Overcoming semantic drift and exact keyword mismatches using Reciprocal Rank Fusion in enterprise knowledge retrieval.

While dense vector embeddings excel at broad conceptual clustering, they frequently fail on specific part numbers, legal citations, and domain terminology.

Hybrid retrieval architectures merge classical BM25 inverted index lookups with high-dimensional vector similarity using Reciprocal Rank Fusion (RRF).

Deploying a cross-encoder reranker further refines candidate passages, boosting downstream generation accuracy by over 35%.

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  • Marcus Vance (collapsed)
    I swore I was going to keep our stack simple this year, and then an article on Hybrid RAG Architectures: Combining BM25 with Dense Semantic Embeddings convinced me that our current setup is practically running on duct tape and prayers. Please tell me I am not the only one who does this.
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