Typed Decisions with Jev

Most language models are designed to generate text. When an application needs a decision, that often means asking a model for JSON, validating the response, and extracting the value that controls the next step. Jev is a System One decision model. Instead of generating prose, it evaluates a state against questions with predefined answer types and returns machine-ready judgments.

Jev and the Decisions API are in beta. Availability and behavior may change without notice.

A support ticket becomes a decision

Suppose this message arrives:

My payouts have failed for three days and nobody has replied. Please help ASAP.

Your application needs to know where to route it, whether it is urgent, and how frustrated the customer appears. Send the message once and ask all three questions together:

cURL

curl https://api.venice.ai/api/v1/decisions \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "jev-latest",
    "state": "My payouts have failed for three days and nobody has replied. Please help ASAP.",
    "questions": {
      "is_urgent": {
        "type": "noul",
        "instructions": "Does this message require urgent attention?"
      },
      "department": {
        "type": "choice",
        "instructions": "Which team should handle this ticket?",
        "criteria": {
          "billing": "Payments, invoices, or refunds",
          "technical": "Bugs, outages, or integrations",
          "sales": "Pricing, upgrades, or new accounts"
        }
      },
      "frustration": {
        "type": "score",
        "instructions": "How frustrated is the customer?",
        "criteria": ["Calm", "Frustrated", "Very angry"]
      }
    }
  }'

Jev returns one answer under each question ID:

{
  "model": "jev-latest",
  "answers": {
    "is_urgent": {
      "type": "noul",
      "noul": 0.95
    },
    "department": {
      "type": "choice",
      "choice": "billing",
      "probabilities": {
        "billing": 0.95,
        "technical": 0.05,
        "sales": 0
      },
      "confidence": 0.93
    },
    "frustration": {
      "type": "score",
      "score": 1.27,
      "legend": {
        "0": "Calm",
        "1": "Frustrated",
        "2": "Very angry"
      },
      "probabilities": {
        "0": 0,
        "1": 0.73,
        "2": 0.27
      },
      "confidence": 0.6
    }
  },
  "usage": {
    "input_tokens": 429,
    "output_tokens": 73
  }
}

Probabilities vary between requests. Evaluate Jev on examples from your own application before choosing production thresholds.

Choose the answer shape

Jev supports three question types:

Type	Question	Answer
`noul`	Is this statement true?	A probability from `0` (no) to `1` (yes)
`choice`	Which defined option fits best?	The selected option, every option’s probability, and confidence
`score`	Where does this fall on an ordered rubric?	A weighted score, level legend, probability distribution, and confidence

Noul: make a binary judgment

Use Noul when the probability of yes is directly useful:

{
  "refund_requested": {
    "type": "noul",
    "instructions": "Does the customer explicitly request a refund?",
    "criteria": {
      "true": "The customer asks for money to be returned",
      "false": "The customer does not ask for money to be returned"
    }
  }
}

Noul has no separate confidence field. A value near 1 is a strong yes, near 0 is a strong no, and near 0.5 is uncertain.

Choice: route or classify

Use Choice when the answer must be one of a closed set:

{
  "request_type": {
    "type": "choice",
    "instructions": "What is the customer's primary request?",
    "criteria": {
      "refund": "Return money already paid",
      "troubleshooting": "Help resolve a product problem",
      "information": "Answer a question without taking action",
      "other": null
    }
  }
}

Include an other or none option when the supplied choices may not cover every state.

Score: measure a spectrum

Use Score when the answer falls along ordered levels:

{
  "severity": {
    "type": "score",
    "instructions": "How severe is the reported issue?",
    "criteria": [
      "Cosmetic or no material impact",
      "Workflow is impaired but a workaround exists",
      "Critical workflow is blocked with no workaround"
    ]
  }
}

Level indexes begin at 0. The returned score is probability-weighted, so it can fall between two levels.

Turn confidence into application behavior

Choice and Score answers include both the full distribution and a single confidence value derived from it. This lets your code treat the answer and certainty as separate signals:

const department = result.answers.department;

if (department.confidence >= 0.9) {
  await routeTicket(department.choice);
} else if (department.confidence >= 0.6) {
  await askForConfirmation(department.choice);
} else {
  await sendToHumanReview();
}

Use higher thresholds for actions that are costly, destructive, financial, or difficult to reverse. Confidence does not guarantee correctness; it helps your application decide when not to act automatically. Every question in a request receives the same state and is evaluated independently. A department answer does not become hidden context for the frustration question. Batch independent questions when:

They evaluate the same document, record, conversation, or application state.
Your code may need several answers depending on the first result.
You want to avoid sending the same state in multiple requests.

Make a second request only when its state or available choices genuinely depend on an earlier answer.

Use structured state

state can be a string, JSON object, or array. Structured state lets questions refer to specific records and supporting context:

{
  "ticket": {
    "subject": "Duplicate charge",
    "message": "I was charged twice. Please refund the duplicate."
  },
  "account": {
    "plan": "pro"
  },
  "refund_policy": "Duplicate charges qualify for a refund."
}

Write complete instructions and name the relevant fields, for example: “Does ticket.message request a refund covered by refund_policy?”

Discover Jev and its limits

Use your API key when listing decision models because model availability can differ by account:

cURL

curl "https://api.venice.ai/api/v1/models?type=decision" \
  -H "Authorization: Bearer $VENICE_API_KEY"

The jev-latest model currently supports:

Up to 32,000 tokens for state plus the single longest question
Up to 64,000 tokens for state plus all questions combined
Text or structured JSON input

Treat the Models API as authoritative because pricing, limits, and availability can change.

When to use another model

Use Jev for bounded judgments your software can act on directly. Use a chat or reasoning model when you need:

Generated prose or explanations
Multi-turn conversation
Tool calling
Open-ended answers
A long chain of dependent reasoning

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A support ticket becomes a decision

Choose the answer shape

Noul: make a binary judgment

Choice: route or classify

Score: measure a spectrum

Turn confidence into application behavior

Use structured state

Discover Jev and its limits

When to use another model

Next steps

​A support ticket becomes a decision

​Choose the answer shape

​Noul: make a binary judgment

​Choice: route or classify

​Score: measure a spectrum

​Turn confidence into application behavior

​Ask related questions together

​Use structured state

​Discover Jev and its limits

​When to use another model

​Next steps

A support ticket becomes a decision

Choose the answer shape

Noul: make a binary judgment

Choice: route or classify

Score: measure a spectrum

Turn confidence into application behavior

Ask related questions together

Use structured state

Discover Jev and its limits

When to use another model

Next steps