Workflow evals

TypeSafe's published evaluations of four automation workflows, comparing Jev and frontier LLMs as structured workflows versus single prompts.

Open evals.typesafe.ai All 177 terms

Category: Evaluation & Training
Also known as: TypeSafe evals
Reading time: 1 min
Related terms: 4
Directory entries: 67
Docs: evals.typesafe.ai
Added: 2026-09-24

Definition

evals.typesafe.ai covers security incidents, agent trace observability, invoice processing, and customer service. Models are scored as workflows of Noul, Choice, and Score questions against consensus labels from GPT-6 Astra and Claude Fable 5.1 at high thinking; Jev averaged 67.8% accuracy at $0.0004 and 0.4 seconds per case.

The headline finding is not about Jev alone: every model was more accurate, cheaper, and faster in a workflow than with the same policy as a single prompt. The evals are vendor-run, and the harness and reference labels are assumed rather than independently audited.

Tagsevaluationbenchmarks

Related terms

Definitions that connect to this one.

Consensus labelsReference answers built from two frontier models at high thinking, used as the ground truth in TypeSafe's workflow evals.Evaluation & Training Cost/quality frontierPlotting accuracy against cost and time on log scales, where up and to the left is better; cascades are designed to sit there.Evaluation & Training Benchmark claimsTypeSafe's headline multipliers (193.6x faster, 444.6x cheaper, a 238x lower input price) come from its own workflow comparisons.Evaluation & Training CalibrationThe property that outcomes given a probability of 0.2 occur about 20% of the time, measured across many predictions.Answers & Confidence

From the Jev directory

Directory entries that go deeper on this term.

Workflow evals

Published evaluations of four automation workflows, security incidents, agent trace observability, invoice processing, and customer service, comparing Jev and frontier LLMs as structured workflows versus single prompts.

Jev Practices & PatternsDocs#official#benchmarks#evaluation

Official

Verifying LLM Answers Against Their Source

Official Jev cookbook that checks whether an LLM's answer follows from the paragraphs it was given, using three Noul questions in one request and a risk score that sends doubtful answers to review.

Jev CookbooksDocs#python#guardrails#evaluation

Community

Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias?

arXiv study that shuffles candidate options across eight presentations while keeping question and candidate content fixed, testing whether Jev-style System One models resist selection bias. Compares two Jev versions, twelve Jev reproductions, and six generative LLMs on 1,700 questions.

Jev Benchmarks & EvaluationsArticle#arxiv#evaluation#calibration

Community

What Is Jev? The 2026 Complete Guide

czmilo's long guide to TypeSafe's System One model: the 193.6x/444.6x benchmark claims, RLCD training, Cloudflare, OpenRouter, and LangChain availability, a 32K context window, and vendor caveats.

Jev Guides & ArticlesArticle#community#article#guide

Community

What Jev's Zero-Hallucination Claim Really Means

Behaviour unpacks Jev's zero-hallucination claim as structural (outputs are confined to a fixed schema, so answers are valid but not necessarily correct) alongside the $40M seed round, Vercel adoption, Arize benchmarks, and prompt-injection findings.

Jev Guides & ArticlesArticle#community#article#safety

Community

Jev 1.14 Release, Reported by MarkTechPost

MarkTechPost's write-up of the Jev 1.14 release: images, a box question type and 262k long context as experimental danger-zone features, unchanged $0.042 per million input token pricing, and TypeSafe's own note that none are production-ready.

Jev Media & TalksArticle#community#news#images

Community

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Posts from builders shipping with Jev right now.

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