Ch 17: Advanced RAG & Knowledge Systems - Intermediate¶
Track: Advanced | Try code in Playground | Back to chapter overview
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You can read this content here on the web. To run the code interactively, either use the Playground or clone the repo and open chapters/chapter-17-advanced-rag-and-knowledge-systems/notebooks/02_fusion_and_reranking.ipynb in Jupyter.
Chapter 17: Advanced RAG — Notebook 02 (Fusion & Reranking)¶
We combine sparse and dense rankings with RRF, then rerank the survivors to push the best result to the top.
What you'll learn¶
| Topic | Section |
|---|---|
| Why fuse rankings instead of scores | §1 |
| Reciprocal Rank Fusion | §2 |
| Reranking a candidate set | §3 |
Time estimate: 3.5 hours
Key concepts¶
- Rank fusion — combining positions avoids the score-calibration problem.
- RRF — a parameter-light, robust fusion that just works.
- Reranking — spend extra compute only on a small candidate set.
RRF fuses sparse and dense rankings without score calibration; a reranker then refines the top candidates. Together they outperform either retriever alone.
Run the full notebook in the chapter folder for code and outputs.
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