Reranker
Also known as: Re-ranker, Cross-encoder reranker
Two-stage retrieval: cheap retriever shortlists, expensive reranker reorders for higher precision.
A second-stage model in a retrieval pipeline that takes the top-K candidates from a cheap retriever (BM25 / dense) and re-scores them with a more expensive cross-encoder or LLM. Dramatically improves top-N precision at modest extra cost.
In practice
Standard quality lever for production RAG. Interviews probe cohere-rerank vs ColBERT vs LLM-as-judge approaches.
Comparisons that include Reranker
Related topics
Questions that mention this term
- Match each 2026 embedding vendor to a defining feature or pricing fact
- Select the practical mitigations for the lost-in-the-middle effect that actually move the needle
- Why does a cross-encoder reranker pay off even though it is slower than a bi-encoder retriever?
- Match each 2026 reranker offering to its distinguishing trait
- Order the steps of canonical embed then rerank 2 stage retrieval
- Order the slots to…
Related terms
AI System Design
End-to-end design of production LLM systems: ingestion, retrieval, serving, eval, monitoring.
BM25
Classical TF-IDF-style lexical scoring: a surprisingly strong baseline, still the first stage of many hybrid RAG pipelines.
Chunking
Cutting documents into retrievable pieces before embedding them.
Context Engineering
Deciding what makes it into the model's context window (prompt + RAG + history + memory), and why.
Context Window
The max number of tokens a model can attend to at once.
Fixed-size Chunking
Slice documents every N tokens with a small overlap. Fast and format-agnostic, but cuts through headings and mid-topic.