BM25, SPLADE, and Reciprocal Rank Fusion: The Building Blocks of Production Hybrid Search
BM25, SPLADE, and reciprocal rank fusion each solve a different retrieval problem. Here's how the three combine into a production hybrid search system.
RAG Pipeline Design
Architecture patterns and retrieval strategies for building retrieval-augmented generation systems that ground LLM responses in external knowledge.
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Hybrid Search →
Hybrid search combines two ways of finding documents: dense vector search, which matches by meaning, and sparse keyword …
7 articles
Retrieval-Augmented Generation →
Retrieval-Augmented Generation (RAG) is an architecture pattern that connects a large language model to an external …
6 articles
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Updated Apr 29, 2026
Concepts covered
What Is Hybrid Search and How BM25 Plus Dense Vectors Beat Either Alone in RAG
Hybrid search fuses BM25 keyword retrieval with dense vector search using reciprocal rank fusion. Why two ranked lists beat either alone in RAG pipelines.
From Chunking to Reranking: RAG Pipeline Components and Prerequisites
Every RAG pipeline runs five components — chunker, embedder, vector store, retriever, reranker. Here is what each one does and where each one breaks.
Score Mismatch, Tuning Hell: The Hard Limits of Hybrid Search Fusion
Hybrid search merges BM25 and vector results, but the fusion step has hard limits. Score mismatch, RRF blindness, and tuning hell — explained.
What Is RAG and How LLMs Use Vector Search to Ground Their Answers
Retrieval-augmented generation pairs an LLM with a vector index so answers are grounded in real documents — not just training data. The mechanism, explained.
Why RAG Still Fails in Production: Retrieval, Chunking, Grounding
RAG fails in production because retrieval, chunking, and grounding hit structural limits — not because of bugs. Why correct retrieval still hallucinates.
