Phase 4: Advanced RAG · ~90 minutes · Python
Hybrid Retrieval with BM25 and Dense Embeddings
Lexical and semantic retrieval fail on opposite query distributions. Hybrid retrieval with reciprocal rank fusion does not interpolate, it votes - and the vote wins on every query class.
Hiring signal: Can build hybrid retrieval with bm25 and dense embeddings end to end
What you will learn
- Implement BM25 from scratch from the Robertson and Sparck Jones formulation, with field weighting, document length normalization, and tunable k1 and b.
- Build a dense retriever on top of a deterministic mock embedding so the loop runs offline.
- Implement reciprocal rank fusion exactly as Cormack, Clarke, and Buettcher published it in 2009, and explain why it dominates score-weighted interpolation.
- Tune the RRF k constant and the per-modality weights and read the trade-offs on a small fixture corpus.
Introduction
Type: Build Languages: Python Prerequisites: Phase 11 lessons 04 (embeddings), 06 (RAG); Phase 19 Track B foundations (lessons 20-29); Phase 19 lesson 64 (chunking strategies) Time: ~90 minutes
Objective
Learning objectives
- Implement BM25 from scratch from the Robertson and Sparck Jones formulation, with field weighting, document length normalization, and tunable k1 and b.
- Build a dense retriever on top of a deterministic mock embedding so the loop runs offline.
- Implement reciprocal rank fusion exactly as Cormack, Clarke, and Buettcher published it in 2009, and explain why it dominates score-weighted interpolation.
- Tune the RRF k constant and the per-modality weights and read the trade-offs on a small fixture corpus.
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