Should I just ask Jev one big vague question?

Avoid it. The phishing bench cited scores one vague should-I-click question at 62.6%, below a regex baseline, while five atomic signals plus regression reach 95%. Atomization is the difference.

When should I keep my regex instead of Jev?

When a rule already clears about 90% and you do not need probabilities or low latency. The guide keeps regex for that case and reserves Jev for routing, scoring, and gating where closed options help.

Can I trust the cookbook outputs on my own data?

No. Each cookbook was executed against the live API before commit, but outputs shift with keys, models, and data. Treat ticket triage, guardrail, rerank, and composite scoring as patterns to re-run and threshold yourself.

Do I need Jev or is a structured-output LLM enough?

If you need free text, explanations, or open-ended answers, use an LLM. Choose Jev when answers are closed options, scores, or yes-no, especially with high volume, tight latency, or confidence-gated actions.

What are the four cookbooks?

Ticket triage (mixing Choice, Score, and Noul in one parallel call), a confidence-gated content guardrail, RAG reranking, and composite scoring where you decompose a big judgment and weight it in code; each ran against the live API before commit.

How do I run a cookbook?

cd cookbooks, copy .env.example to .env and paste your key, then run a script such as uv run 01_ticket_triage.py; the guide uses uv and the typesafe-sdk package.

Does it explain how to get a key?

Yes: get one at typesafe.ai, or use Vercel AI Gateway with the typesafe-ai/jev model, which the guide notes has no waitlist.

CommunityJev Guides & Articles

Awesome Jev — dog-last

Guide plus runnable cookbooks for deciding when Jev fits, favoring decomposed atomic questions with confidence gating over single vague prompts.

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Language: Python
License: MIT
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Last verified: Oct 2026

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A curated guide to Jev, TypeSafe AI's System One decision model — selection advice, API-verified cookbooks, independent evaluations, and 100+ community. 中英双语

Forks: 1
Last pushed: Updated Sep 2026

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This is one of several Jev listings for the same project. The canonical entry is Awesome Jev — yibie.

About this project

The guide that answers "should I use Jev, and how" — not just another link directory. English | 简体中文 | 🌐 Web Version. The most expensive lesson from independent evals: asking Jev one big vague question is the worst way to use it. On a phishing benchmark, asking "should I click this?" scored 62.6% — worse than a regex (91.8%). Decomposing the same task into 5 atomic signals + logistic regression hit 95.0%. "Question atomization" was worth 32 points. (jev-phishing-bench)

Quoted from the project's README. Read the full README.

Tagscommunity github

Works forJev for Evaluation

Used inJev for Software & DevTools

Languages: Python

Highlights

Gives a decision tree over closed options, regex baselines, explanation needs, and latency or volume.
Shows one vague phishing question failing while atomic signals plus regression score far higher.
Ships four live-tested cookbooks for triage, guardrails, RAG rerank, and composite scoring.
Quickstarts with the typesafe SDK and documents Vercel AI Gateway as an alternate key route.

Watch out

Guide with live-tested examples; adapt thresholds and decomposition to your data rather than copying prompts verbatim. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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