Use Jev for a bounded decision—such as classifying a ticket or scoring a document—not for writing prose or enforcing rules on its own. Send it a text-based state and typed questions, validate its structured answers, then let application code decide whether to route, rank, filter, or escalate the case.
What Jev does—and what it does not do
TypeSafe AI describes Jev as its flagship and first System One model. Its interface is designed to evaluate typed questions against a supplied state and return structured results. As the vendor puts it, “Jev evaluates typed questions against a state and returns structured results directly. No text generation, no parsing.” See the TypeSafe introduction.
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That makes Jev a decision component for an application or agent, rather than a general-purpose chatbot. Your software supplies relevant evidence, asks for a constrained judgment, checks the result, and handles what happens next. Use a generative model when you need new prose; use ordinary code for exact calculations, permissions, and business rules.
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System One offers three primitives. You can combine them in a request when several distinct judgments are useful, while keeping each question focused.
#1 Best Overall
| Primitive | Use it for | Example |
|---|---|---|
| Choice | Selecting one answer from a defined set of options. | Classify a support ticket as billing, technical, or account access. |
| Score | Evaluating something against an ordered rubric. | Score how relevant a document is to a stated request using explicit criteria. |
| Noul | Estimating the probability that a proposition is true. | Estimate whether the supplied message indicates a duplicate charge. |
Make the question, options, and criteria agree. For example, a Choice question should list the possible outcomes; a Score question should explain what each part of its rubric means. If missing information or uncertainty should lead to review, define that route in your application rather than treating a model probability as proof.
Prepare a bounded request
1. Pick one judgment
Start with a task that has a clear scope and a useful next action: classify a ticket, choose which tool to invoke, score relevance, or flag a document for closer review. Avoid combining independent questions or asking Jev to complete a chain of inference in one go. Break a complex decision into focused questions and combine their results in code.
2. Build the state from relevant evidence
The state is the evidence Jev will judge. For a support decision, it might include the customer’s message and the relevant transaction and policy fields. Retrieve and filter material in your application first; irrelevant context can distract from the question.
Jev’s documented inputs are text-oriented: a string, JSON object, or array of text values. The model reference lists text-only input, not images, audio, or video. Convert non-text material into text or structured fields before sending it. Consult the TypeSafe model reference for the current input and context specifications.
3. Write explicit questions and criteria
Ask one thing at a time where possible, and make edge cases legible. If “unknown” is materially different from “no,” provide a way for your application to handle insufficient evidence; do not let a forced answer conceal ambiguity. For multi-factor decisions, ask separate focused questions and apply the combination rule in your code.
Make a first API request
The hosted System One API reference documents an authenticated JSON request to POST /v1/systemone. Its example uses a model alias, a shared state, and a named Choice question. It specifies an API key as a bearer token. Create a key through the service you intend to use, then follow that service’s current endpoint, key-scope, and account instructions.
The documented hosted base is https://system-one.dev/v1, so the request path is https://system-one.dev/v1/systemone. The host and account or credit arrangements are service-specific; do not assume that a gateway’s credits, request identifiers, or key handling also describe direct TypeSafe API access. Follow the System One API reference for the service’s exact request and response schema.
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{
"model": "jev-latest",
"state": {
"message": "I was charged twice for the same order.",
"order_id": "ORDER-123",
"policy": "Escalate only when duplicate charges are indicated."
},
"questions": {
"category": {
"type": "choice",
"options": ["billing", "technical", "account access"]
}
}
}Send the JSON to the endpoint with an Authorization: Bearer YOUR_API_KEY header and the appropriate JSON content type. Use the API reference’s exact field names and supported question syntax when implementing a real client; the illustration is not a substitute for that schema.
Validate the result before acting
Handle Jev’s response as input to program logic, not as an instruction to execute blindly. Validate that the expected question names and answer shapes are present, then apply application-owned rules to the result.
- Set thresholds based on the consequences of an error, not on convenience.
- Route low-confidence or ambiguous cases to a person or another review path.
- Keep exact calculations, date comparisons, access control, and money movement in deterministic code.
- Do not use a model judgment by itself to grant permissions or enforce a precise policy.
The System One introduction and independent Jev overview both support dividing work this way: use Jev for bounded semantic judgments, code for exact operations and permissions, and a generative model when the task is to produce text.
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As listed by TypeSafe AI in its model reference checked in 2026, Jev 1.13 had the version identifier jev-1.13.0, and jev-latest pointed to it at that time. The alias follows the latest stable release and can change. TypeSafe says the response contains the versioned model ID; log that resolved ID, and pin a specific version when stable thresholds or reproducibility matter.
The same TypeSafe reference listed a 64k-token total context ceiling per request, with a 32k-token bound for the state plus the longest question. The total budget covers the state and all questions. Treat these as vendor-listed limits checked in 2026, not as permanent service guarantees; check the model page for current limits before deployment.
TypeSafe’s 2026 listing for Jev 1.13 showed input pricing of $42 per billion tokens ($0.042 per million) and output tokens listed as free. These are vendor-published figures, not independent measurements or a promise of future pricing. Confirm the current price and which service path it applies to before estimating costs.
The model page also listed 100K tokens per second and 80 requests per second, while warning that rate limits are being adjusted dynamically and may change without notice. Do not design capacity around those figures as guaranteed quotas; check your account’s applicable limits.
Test the decision path, including its failure modes
TypeSafe’s version-scoped Jev 1.13 notes, reviewed 2026-10-02, warn that the model can read instructions literally, struggle with extra indirection, and perform poorly at arithmetic, dates, and counting. They also describe sensitivity to irrelevant context, adversarial text, and option order, as well as a mismatch when a question or criteria do not fit the task. The model is not trained to generate text. See the model reference and its version notes for the vendor’s current caveats.
Before relying on a workflow, make fixtures for the cases most likely to change the action your software takes:
- Boundary cases at each category or score threshold.
- Missing, ambiguous, or contradictory evidence.
- Negation and indirect wording.
- Irrelevant context mixed with useful evidence.
- Adversarial text that attempts to change the intended judgment.
- Changes to question wording, criteria, state construction, option order, or model version.
Run those fixtures whenever you change a consequential part of the pipeline. If a task requires exactness or several inference steps, move those operations into code or evaluate a different approach rather than assuming that a more elaborate prompt removes the limitation.
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