Lesson 02 · Jev 101
How Jev works: state + typed questions
Forget prompt engineering for a moment. Every Jev call has the same shape: state in, questions in, typed answers out.
State (your context)
State is whatever the decision is about: a ticket, email, agent trace, document, game state, log blob—often unstructured text, sometimes structured program state (company emphasis). Official docs: a string, a JSON object, or an array of text. Text only — no image, audio, or video yet.
ChatGPT parallel: the stuff you paste into the prompt.
Difference: you’re not also asking Jev to write the answer in English.
Official jaggedness note: accuracy falls when you stuff large irrelevant state (“context rot”). Filter. Send only what the decision needs.
Three question types
| Type | Plain English | ChatGPT prompt you might retire |
|---|---|---|
| Choice | Pick from labels you define. Returns the option, a distribution, and confidence. | “Classify as A/B/C. Return JSON.” |
| Score | Place on an ordered scale / rubric you define. Returns a weighted score plus confidence. | “Rate 1–5 severity.” |
| Noul | Yes/no-style probability (0–1). No separate confidence field — the probability is the signal. | “Is this spam? Answer yes or no.” |
Multiple questions can be evaluated together in one request (company: independently, in parallel). Official theme: speculative fan-out — ask more than you might need, let code select. Launch blog: Choice cardinality up to 255. Score docs: at least two levels; this 101 does not invent a ceiling.
TypeSafe docs · how to build · fetched Sep 20, 2026 · Source“System One is TypeSafe's model for building AI-powered software, not agents.”
What you do not get back
Jev does not return essays, code, tool calls, or chain-of-thought prose. Official docs: “jev-1.13 is not trained to generate text.” If you need an explanation for a human, call an LLM after (or beside) the decision—or show your own UI copy.
TypeSafe docs · jaggedness · reviewed Sep 17, 2026 · Source“jev-1.13 is not trained to generate text.”
TypeSafe docs · jaggedness · Sep 17, 2026 · Source“Jev is not a calculator. We strongly recommend implementing any mathematical logic in code.”
Why this feels more like code
Your application defines the answer space before inference. That constraint is the product: TypeSafe claims the model never makes type errors; schema matching is guaranteed by constructionCompany claim. You still design for wrong decisions via confidence and escalation.
TypeSafe launch blog · Sep 15, 2026 · Source“No type errors: This would be an easy thing to falsify with just a single counter-example, but it is mathematically impossible.”
TypeSafe manifesto · fetched Sep 20, 2026 · Source“We want AI to work alongside existing software as a primitive that any programmer can invoke for semantic judgement and decisions, while still using code for what it’s best at: exact computation.”
Constraints worth knowing
- Context cited in models.md: 64k for state + all questions; 32k for state + longest question
- English is primary; CJK and other languages are accepted with lower accuracy
- No customer fine-tune / LoRA; same weights for all accounts
- Not trained on customer requests or responses (official models.md)
- State is data, not a hostile user by default — add your own injection defenses
Optional code · official surface
Endpoint from the official quickstart. Do not freeze a request body here — copy the current schema fromdocs.typesafe.ai.
POST https://api.typesafe.ai/v1/systemone Authorization: Bearer $TYPESAFE_API_KEY # Python: pip install typesafe-sdk # JS: @typesafe-ai/sdk # Pin: jev-1.13.0 (aliases jev-latest / jev-preview can move)