Reranker (Cross-Encoder) — AI Glossary

Reference details

Related terms

• RAG (Retrieval-Augmented Generation)
• Hybrid Search
• Embedding

Example: Why a second model earns its latency

A bi-encoder embeds query and document separately, so the two never interact until a dot product at the end — fast enough to scan millions. A cross-encoder feeds the pair through together, letting every query token attend to every document token, which is far more accurate and far too slow for the whole corpus. So you retrieve 100 cheaply, then rerank those 100 properly.

Commonly confused with

A reranker is the model; reranking is the pipeline stage it occupies. A reranker cannot improve recall — it only reorders what retrieval already returned. If the right document was not in the top 100, no reranker will surface it.

When to use it

Reach for it when:

• Retrieval recall is acceptable but the top few results are poorly ordered
• You feed a small number of passages into a context-limited prompt
• Precision at position 1–3 is what actually drives answer quality

Reach for something else when:

• Recall is the problem — fix retrieval first; reranking cannot recover a miss
• Latency budget is tight and the ordering is already good enough
• The candidate set is already small enough to pass through whole

Referenced by

This term is used by the following ModelRefs references:

• long term memory rag
• vector search tool
• naive rag stack
• advanced rag stack

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