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)