Jev glossary
PatternsReranking
Re-ranking
Score each query-candidate pair with one question and reorder a shortlist; the CLERC example lifts top-1 accuracy from 5% to 18%.
- Category
- Patterns
- Also known as
- Reranking
- Reading time
- 1 min
- Related terms
- 4
- Directory entries
- 15
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
The re-ranking cookbook builds 30-passage BM25 shortlists for 40 CLERC legal queries, then asks one question per query-candidate pair. Top-1 accuracy rises from 5% to 18% and top-10 from 38% to 62%.
Pairwise questions give the model a narrow comparison instead of a whole-document judgment, and the resulting probabilities can be sorted directly: a natural fit for search and retrieval pipelines.
Tagspatternssearchreranking
Related terms
Definitions that connect to this one.
Line-by-line searchRank every line of a document in one request with a Choice, while a Noul checks whether the document contains an answer at all.PatternsRAG passage classificationScore each retrieved passage with four Noul questions, then decide in code which passages reach the answering model.PatternsParallel beam searchKeep the top-k branches at each taxonomy level instead of only the winner, so a wrong first choice can still be recovered.PatternsScoreRate the state against 2 to 10 ordered levels; returns a probability-weighted position, per-level probabilities, and confidence.Primitives
From the Jev directory
Directory entries that go deeper on this term.
One question per query-candidate pair raises top-1 accuracy from 5% to 18% and top-10 accuracy from 38% to 62% on 40 CLERC legal queries.
Jev CookbooksDocs#official#cookbook#reranking
Official
Rank 182 agent skills in one request and re-read the top three in a second: over 488 requests, wrong skill loads fell from 16.8% to 7.3% and needless loads from 9.8% to 4.0%.
Jev CookbooksDocs#official#cookbook#agents
Official
cnfeichen's reproducible Chinese reranking benchmark for Jev over hard negatives from C-MTEB/T2Reranking: a 4-level Jev Score rubric reaches 0.546 nDCG@5 on 100 queries against 0.413 for BM25 with jieba. Independent community research, not affiliated with TypeSafe. MIT.
Jev Benchmarks & EvaluationsDocs#community#evaluation#reranking
Community
Elastic's Dustin Coates scores Elasticsearch hybrid search results with Jev and reranks them with a small Python policy, lifting nDCG@10 from 0.9351 to 0.9565 and Exact MRR from 0.9201 to 0.9616 across 250 Amazon Shopping Queries.
Jev Guides & ArticlesArticle#community#article#reranking
Community
Plugs Jev-style decision models into published rerankers as a fast primitive: one forward pass over k candidates, zero decoded tokens. On T2Ranking it reports up to 22x faster matches than a generative listwise reranker at 2.9x the nDCG@10.
Jev Repos & SDKs#community#catalog-import#yibie
Community
hgqimo/jevrankerPython
hgqimo/jevrankerPythonAn independent evaluation of Jev in three retrieval stacks, with recalculated recall, latency, and cost against DeepSeek, GPT-4o-mini, and GPT-5-mini baselines.
Jev Practices & PatternsArticle#community#search#reranking
Community
9 more matching entries in the full directory.
From the Jev AI model community
Posts from builders shipping with Jev right now.