The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Use Jev for a bounded judgment over text or structured state, then let your application validate and act on its structured answer. Jev is TypeSafe AI’s first System One model: it answers typed questions with a choice, score, or probability rather than writing prose. That makes it useful for tasks such as classifying a support ticket or scoring a document, but not a substitute for deterministic code, a text-generating model, or human review when the stakes are high.
What Jev does—and what it does not do
TypeSafe describes Jev as its flagship and first System One model. Its interface pairs a state—the evidence to evaluate—with focused, typed questions. The result is structured data that application code can inspect. As TypeSafe puts it, “Jev evaluates typed questions against a state and returns structured results directly. No text generation, no parsing.” TypeSafe’s Introduction
Think of Jev as a decision component inside a larger system. For example, your code can retrieve a support message and relevant account or policy fields, ask Jev to classify the case, validate its answer, and route the ticket. Code should still perform exact calculations and enforce permissions; use a generative model when the task is to compose new text.
Choose the right question type
System One provides three primitives. Use the one that matches the shape of the answer you need.
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| Primitive | Use it for | Example |
|---|---|---|
| Choice | Selecting one answer from a defined set of options. | Classify a ticket as “billing,” “technical,” or “account access.” |
| Score | Evaluating a case against an ordered rubric. | Rate how relevant a document is to a request using stated criteria. |
| Noul | Estimating the probability that a specified statement is true. | Estimate whether a message reports a failed payment. |
You can ask multiple typed questions against the same state. Keep each question focused on one judgment: if a decision depends on independent factors or several inference steps, ask about those factors separately and combine the results in code. Define the options or scoring criteria explicitly, and provide an uncertain or review route in your application rather than treating a probability as proof. TypeSafe’s Introduction and System One primitives
Prepare the state and questions
Build a relevant state
Include the evidence needed to answer the question, not the entire contents of every source you can retrieve. For a support classification, that might be the customer’s message plus the transaction and policy fields relevant to the issue. Filter unrelated material before sending it: Jev can be sensitive to irrelevant context, and adversarial text in the state can affect its answer.
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TypeSafe’s model reference describes Jev 1.13 as text-only. It accepts text-oriented state such as a string, JSON object, or array of text values; it does not take images, audio, or video directly. Convert non-text inputs into text or structured fields before making the request. TypeSafe’s Models reference
Make the judgment explicit
Ask a direct question and ensure its criteria match the answer type. For a Choice question, specify a closed set of meaningful options. For a Score question, define what the scale means, including important edge cases. For Noul, state one proposition whose truth probability is being assessed. Do not ask Jev to calculate a date difference, count items, or infer a long chain of unstated assumptions.
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Make a first API request
The hosted System One API reference documents an authenticated JSON request to POST /v1/systemone, with a model identifier, shared state, and named questions. It shows a bearer token for authentication and a Choice question in its example. The documented hosted base URL is https://system-one.dev/v1. The following is a structural example; use a valid key and the precise request shape required by the current API reference.
POST https://system-one.dev/v1/systemone
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json
{
"model": "jev-latest",
"state": {
"message": "I was charged twice for my order."
},
"questions": {
"category": {
"type": "choice",
"options": ["billing", "technical", "account access"]
}
}
}Create an API key through the service account you intend to use, and follow that service’s current instructions for key scope and account credits. The System One hosted API reference says successful evaluations consume account credits. Do not assume its endpoint, credit system, key handling, or request identifiers apply to a separate direct TypeSafe access path. System One API reference
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Handle the response in application code
Read the response as model output, not as an instruction to execute blindly. Validate that the returned question names and value types match your expected schema. Then apply application-specific thresholds and routing. For example, a low-confidence result or a high-impact case can go to human review; a well-formed classification can route a ticket. Keep money movement, access control, and exact policy enforcement in independently checked code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Account for Jev 1.13’s limits
At the time TypeSafe’s model page was checked in 2026, it listed Jev 1.13 as jev-1.13.0, with jev-latest pointing to that release. TypeSafe’s version-scoped notes, marked reviewed 2026-10-02, warn that Jev can interpret wording literally, struggle with extra indirection, and perform poorly at numeric precision. It is also sensitive to irrelevant context and adversarial input, and it is not trained to generate text. TypeSafe’s Models reference and Jev 1.13 limitations
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- Use ordinary code for arithmetic, date comparisons, counting, permissions, and other exact rules.
- Filter irrelevant state and phrase questions directly.
- Write precise scoring criteria, especially for edge cases.
- Use a text-generating model for drafting explanations or other new prose.
- Test behavior against adversarial content before relying on a judgment in a consequential workflow.
Test the integration before relying on it
Create representative test cases for the actual decision you are automating, not just easy examples. Include boundary cases, missing information, contradictory evidence, negation, and adversarially phrased content. Check Jev’s answer shape as well as its judgment, and test the code paths for uncertainty, invalid values, and escalation.
Repeat those checks when you change state construction, question wording, criteria, option order, or model version. TypeSafe’s notes identify literal interpretation, mismatched criteria, adversarial content, and option-order effects as observed failure modes. A change that appears cosmetic can alter results, so keep test fixtures alongside the integration.
Choose a version and check service limits
TypeSafe’s Models reference, checked in 2026, listed a 64k-token total context ceiling per request and a 32k-token limit for the state plus the longest question; the total budget covers the state and all questions. It also listed 100K tokens per second and 80 requests per second, while warning that rate limits are adjusted dynamically and may change without notice. Treat these as the vendor’s listed service figures, not durable guarantees. TypeSafe’s Models reference
The same 2026 listing priced Jev 1.13 at $42 per billion input tokens, equivalently $0.042 per million input tokens, with output tokens listed as free. These are TypeSafe-published rates for that listing, not a promise that future pricing will remain the same. Confirm current pricing and limits with the service you use before budgeting or setting production capacity. TypeSafe’s Models reference and TypeSafe pricing
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