reranking-patterns
Reranking Patterns
Improve search precision by re-scoring retrieved documents with more powerful models.
Overview
- Improving precision after initial retrieval
- When bi-encoder embeddings miss semantic nuance
- Combining multiple relevance signals
- Production RAG systems requiring high accuracy
Improve search precision by re-scoring retrieved documents with more powerful models.
Why Rerank?
Initial retrieval (bi-encoder) prioritizes speed over accuracy:
- Bi-encoder: Embeds query and docs separately → fast but approximate
- Cross-encoder/LLM: Processes query+doc together → slow but accurate
Solution: Retrieve many (top-50), rerank few (top-10)
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