Reranker Tuning
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Adds and tunes a reranking stage on top of first-pass vector retrieval to lift precision at the top of the result list. Covers cross-encoder versus LLM-as-reranker choices, candidate pool size, score thresholds and cutoffs, hybrid fusion with keyword search, and the latency and cost budget a reranker introduces.
Варианты использования
- Adding a cross-encoder reranker to an existing RAG pipeline
- Choosing candidate pool size and final cutoff
- Fusing dense and keyword results before reranking
- Measuring the precision gain against added latency and cost
Пример промпта
Our vector search returns roughly relevant chunks but the best passage is often ranked 5th or lower. Design a reranking stage. Recommend cross-encoder versus LLM reranking for our case, the candidate pool size to retrieve before reranking, how to set the final cutoff, and whether to add hybrid keyword fusion. Quantify the expected latency and cost per query, and tell me which metrics should improve if it works.
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OpenModels Community