MMuhammad Tahir·Oct 11Hybrid Search Architecture: Combining BM25 Keyword Search with Dense Vector EmbeddingsThis article was originally published on Muhammad Tahir's Portfolio. Introduction & Industry Context In the early wave of generative AI and Retrieval-Augmented Generation (RAG) deployments, engineerin13 min read·mtdeveloper.hashnode.dev
Mmarket·Sep 28BM25 vs vector vs RRF hybrid on 85 chunks — vector MRR won (hybrid did not “always win”)Canonical: Hybrid BM25 + vector precision on eWorld.liveCheck date: 2026-09-26 PT · API spend $0 (CPU) Self-built 85-chunk Northline Hybrid Lab + 15 graded queries (5 keyword / 5 semantic / 5 mixed).1 min read·eworldlive.hashnode.dev
IInternals Decoded·Sep 28Hybrid Search and Re-Ranking: The Cheapest Quality WinKeywords catch what vectors miss, and re-rankers fix the order. Most teams skip both.13 min read·internals-decoded.hashnode.dev
VViv Chaudhary·Sep 8Build a Solid RAG System in One Weekend (2026 Edition)The four decisions that actually determine whether your RAG system works - chunking, retrieval, reranking, and evaluation with the trade-offs the tutorials usually skip.9 min read·truenorthbits.hashnode.dev2VA
AAman Jain·Aug 2How RAG Finds the Right Evidence: Dense, Sparse, and Hybrid RetrievalComparing semantic search, BM25, Reciprocal Rank Fusion, evidence grading, and Corrective RAG10 min read·curious-pm.hashnode.dev
VVarun Vaibhav.S·Jul 8How I Automated 3–4 Days of Manual Work at L&T Using PythonA PDF extraction pipeline built during my internship at L&T Construction — FAISS, BM25, PyMuPDF, and Python6 min read·theflighttechlabs.hashnode.dev1
SSukalyan Roy·Jun 20Adding Rank Fusion to my RAG retrieval logicMore on Agentic Rags and Project updates13 min read·sukalyanroy.hashnode.dev
RRahul Sehrawat·Apr 13Hybrid Search: BM25 + Vectors Beats Either AloneDense retrieval misses keywords. Sparse retrieval misses semantics. Real RAG uses both, fuses the results, and ships a product that finds your product names, error codes, and edge cases.11 min read·ai-zero-to-hero.hashnode.dev
EElisé Baraka M.·Apr 7Building a RAG Engine — Part 1: Retrieval FoundationBuilding the search layer of a RAG engine with provenance, hybrid retrieval, grounded citations, multilingual handling, and evaluation.14 min read·ebmurha.hashnode.dev
RRavi Mohta·Mar 17In, Store, Retrieve: The Three Places RAG Quietly Fails — And How We Fixed Each OneWhat five months of building a private AI document intelligence system taught me about retrieval quality, LLM reliability, and the gap between "it works" and "it works well"18 min read·herald-ai.hashnode.dev
