Confidence
Official TypeSafe docs page explaining how Jev's confidence is derived from the probability distribution and how to set thresholds.
This summary is based on how curated lists describe the project; its README was not available to review.
Overview
This page in TypeSafe's documentation explains the confidence value returned with Jev answers. According to the list summaries, confidence is a statistic computed from the probabilities distribution: the more concentrated the distribution, the higher the confidence. Choice and Score answers carry confidence, while Noul answers do not carry a separate confidence field because the answer probability already plays that role.
The page also states that users are not locked into TypeSafe's definition and advises starting thresholds at a conservative value and measuring on your own data. It is essential reading for anyone building gates, fallbacks or escalation rules on top of Jev outputs.
What the decision model decides
| Type | Question | Answers |
|---|---|---|
| Other | How should confidence be interpreted and thresholded for Choice and Score answers?(inferred) | — |
How it works
- Read probabilities and confidence from a Jev response
- Pick a conservative starting threshold
- Measure accuracy above and below the threshold on your data
- Adjust thresholds or compute your own statistic from probabilities
Strengths
- Clarifies the difference between answer probability and confidence
- Encourages data-driven threshold setting
Caveats
- List-only entry; details paraphrased from summaries
- Documentation may change between model versions
Getting started
Read https://docs.typesafe.ai/confidence.At a glance
- Type
- Article
- Status
- production
- Has code
- no
- Has tests
- no
Use cases and integrations
How curated lists describe it
The official documentation explicitly states that `confidence` is a **statistic calculated from the `probabilities` distribution**; the more concentrated the distribution, the more certain it is; **Noul does not carry confidence**; it explicitly says "you are not locked into our definition"; thresholds should "start from a conservative value and measure on your own data."
wh000wh000/awesome-jev-liveDistinguishes Choice/Score confidence from answer probability; Noul has no separate confidence field.
cobanov/awesome-jev
Frequently asked questions
Does Noul return confidence?
According to the docs summary, no; Noul has no separate confidence field.
How is confidence computed?
As a statistic over the probabilities distribution; more concentrated distributions give higher confidence.
What threshold should I use?
The docs advise starting conservatively and measuring on your own data.
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LibraryresearchPythonPreprint survey of Jev-style typed decision models: their research roots, adaptation costs and what current evidence supports.
ModelresearchAgent skill and CLI that chains Jev question design: find decision calls, check fit, draft, lint, calibrate thresholds and plan a cascade.
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