Jev versus high-recall systems: a practical comparison
A high-recall system would rather over-include (search, safety, fraud holds). Jev will not magically maximize recall. You choose thresholds and whether other exists.
Unofficial. Pair with the high-precision page. docs.typesafe.ai.
Comparison scope
Retrieval, keyword OR-lists, and “hold if any signal” rules are recall tools. Jev is a scorer you can set low to keep more candidates for humans.
Criteria that decide the architecture
| Axis | Jev (System One) | High-recall systems |
|---|---|---|
| Miss cost vs review cost | You set τ | OR-rules / wide retrieval |
| Closed set | Choice can drop unlabeled classes unless other |
Wide nets catch unknowns |
| Calibration | Measure on your labels | Usually uncalibrated hits |
Decision quality and control
If missing a case is catastrophic, do not use a single high τ. Use Jev to rank a wide candidate set from search/rules, then humans.
Integration trade-offs
Recall layer (search/rules) → Jev Score/Noul → sort → review until budget. Do not delete the recall layer.
TypeSafe’s public models page lists jev-1.13 at $0.042 per million input tokens with output tokens free — a vendor claim, not a jev.pro measurement. Confirm on the models page before you bid.
When each approach fits
Prefer Jev when
- You have a shortlist and want a ranking / second filter
Prefer High-recall systems when
- You still need the wide net
High recall first, Jev to spend reviewer time, precision page for auto-act.
What this page does not claim
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No recall@k vs BM25 published here.
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Schema-safe output is not the same as factual correctness.
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No independent bake-off numbers live here.
FAQ
Does a low Noul threshold raise recall? It raises how often you treat “yes” as true. Measure misses; don’t assume.
Is other good for recall?
It prevents forced wrong labels. Unknowns still need a recall path.
Disclaimer
This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai. Never treat jev.pro as TypeSafe official documentation. We do not sell, issue, or proxy API keys.
Hub: Comparisons. Siblings: vs high precision systems, vs semantic search, uncertainty handling. Canonical: https://docs.typesafe.ai.
Sources
Public TypeSafe or adjacent documentation only. No private claims.