Reranking

A second retrieval stage that re-scores an initial set of candidate passages with a more precise model, promoting the most relevant to the top.

Reranking is how an agent spends a small context budget on the highest-signal passages — the retrieval-side counterpart to writing content an engine can rank cleanly.

term
Reranking
category
knowledge-memory
short_def
A second retrieval stage that re-scores an initial set of candidate passages with a more precise model, promoting the most relevant to the top.
long_def
Reranking improves retrieval precision: a fast first stage (vector or keyword search) returns many candidates, then a slower, more accurate cross-encoder re-scores each against the query and reorders them. The agent then reads only the top few, so quality rises without a larger context budget.
see_also
rag semantic-search chunking
etymology_origin
— verify-against-primary-at-build ↗ https://en.wikipedia.org/wiki/Learning_to_rank — reranking builds on the learning-to-rank tradition in information retrieval
related_to
rag semantic-search chunking embeddings
contrast_with
Unlike first-stage vector search optimized for recall and speed, a reranker optimizes for precision — it is slower but far better at ordering the final few results.
example
A search returns 50 candidate passages; a reranker re-scores them and moves the 3 truly relevant ones to the top, which is all the agent reads.
source
https://en.wikipedia.org/wiki/Learning_to_rank
status
active
why_it_matters
Reranking is how an agent spends a small context budget on the highest-signal passages — the retrieval-side counterpart to writing content an engine can rank cleanly.
sameAs
https://en.wikipedia.org/wiki/Learning_to_rank
bridge_entity
rag
last_verified
2026-07-06
md_twin
/glossary/reranking.md

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