𝐁𝐌𝟐𝟓 𝐯𝐬 𝐃𝐞𝐧𝐬𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥: 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐈𝐭 𝐨𝐧 𝐘𝐨𝐮𝐫 𝐎𝐰𝐧 𝐂𝐨𝐫𝐩𝐮𝐬

You're probably running the wrong retriever for your RAG pipeline. And you won't know until you actually measure it on your data with real relevance judgments.

The conventional wisdom - BM25 wins on exact matches, dense wins on paraphrases - doesn't survive real corpora. On the corpus in this tutorial, 167 of 300 queries score identically on both arms. A simple tokenizer flag on BM25 gained 0.0246 NDCG@10 for under a millisecond per query. A cross-encoder reranker made retrieval worse while costing four orders of magnitude more.

What you need is a bake-off harness. Run BM25, dense, hybrid (RRF), and reranked retrieval over a labelled query set. Score them on Recall@k, NDCG@k, latency, and cost per query. See which one actually wins on your corpus.

This guide walks you through building exactly that. You'll encode a corpus, run five retrieval arms in parallel, compute three standard IR metrics that tell you where each strategy breaks, and interpret the results with enough statistical rigor to separate signal from noise. The harness works on CPU. No GPU, no API keys, no credentials - everything runs locally in about 90 minutes.

By the end you'll have a repeatable framework to measure any retriever against your own data, stop guessing, and ship the right strategy the first time.

Read the full guide and build the harness yourself:

https://ranjankumar.in/bm25-vs-dense-retrieval-for-rag-engineers

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