Using Jev

Quick start

Run an evaluation in the playground in three steps, then call the same model from your own code.

Question types

TypeSafe exposes three AI primitives. Each asks a different kind of question and returns a different kind of answer.

Request parameters

Every question object shares instructions (what to decide or rate) and adds its own criteria: an option map for Choice, an ordered level array for Score, and optional true/false descriptions for Noul.

Answers

One answer per question, keyed by the ids you supplied. Choice and Score answers carry a confidence value derived from their probability distribution; Noul answers return a probability only.

Every response also reports token usage for the request as usage.input_tokens and usage.output_tokens.

Confidence

The shape of a probability distribution is what tells you how certain the model is: concentrated on one outcome is confident, spread across several is not. The confidence value collapses that shape into a single number from 0 to 1, so you can threshold on it without doing the maths yourself. A useful starting pattern is three bands:

Where you draw the boundaries depends on the stakes: gate a destructive action at a higher threshold than a read-only one.

Full example

One call, three questions, one response — no SDK required.

const res = await fetch("https://jevstation.com/api/v1/systemone", {
  method: "POST",
  headers: {
    Authorization: "Bearer " + process.env.JEVSTATION_API_KEY,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    state: "Hi, I've been trying to connect my Stripe account for 3 days and it keeps failing. I'm losing sales. Please help ASAP.",
    questions: {
      department: {
        type: "choice",
        instructions: "Which team should handle this?",
        criteria: {
          billing: "Payments, invoicing, refunds",
          technical: "Bugs, outages, integrations",
          sales: "Pricing, upgrades, new accounts",
        },
      },
      frustration: {
        type: "score",
        instructions: "How frustrated is the customer?",
        criteria: ["Calm", "Frustrated", "Very angry"],
      },
      is_urgent: {
        type: "noul",
        instructions: "Does this convey urgency?",
      },
    },
  }),
});

const { data } = await res.json();

data.answers.department.choice;      // "billing"
data.answers.department.confidence;  // 0.59
data.answers.frustration.score;      // 1.04
data.answers.is_urgent.noul;         // 0.99
data.credits.charged;                // 1
data.credits.remaining;              // 4,996

curl -X POST https://jevstation.com/api/v1/systemone \
  -H "Authorization: Bearer $JEVSTATION_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "state": "Payouts have been failing for 3 days.",
    "questions": {
      "is_urgent": { "type": "noul", "instructions": "Does this convey urgency?" }
    }
  }'

Notes & limits

• An evaluation is a single round trip: Jev returns answers, not a token stream, so a run either produces a complete result or fails cleanly.
• Adding questions to a set costs extra input tokens but barely any extra latency, because they are evaluated in parallel against the same state.
• Each run consumes credits: one for a standard evaluation, three once the state passes 8,000 characters or the set passes five questions. Clef doubles both (2 and 6); Jev and Clef-flash do not. The balance is checked before the model is called, and a failed or cancelled run is refunded.
• Evaluations are scoped to the signed-in user; one account can never read another's.
• Provider credentials are configured by the operator in the admin settings and never reach the browser.
• On 429 or 529, retry with exponential backoff. The client SDKs already do this for you.