Reference · Glossary
Cross-encoder
A model that **scores query–document pairs together** — slower than bi-encoders but sharper for re-ranking top candidates.
When to use
Re-ranking 20–50 hybrid retrieval hits before the LLM context window.
When not to
First-stage retrieval over millions of docs — use vector index instead.
Example
Merge keyword + vector top-20 → cross-encoder scores each pair → keep top-5 for RAG.