Score

A Score question rates the state on an ordered rubric. Use it for intensity, severity, relevance and priority — any judgment that lives on a scale rather than in a category.

When should I use Score?

How urgent is this case?
How relevant is this passage to the query?
How severe is this policy violation?
How well does this response match the request?

If the decision is a category, use Choice. If it is a boolean, use Noul.

Request shape

{
  "type": "score",
  "instructions": "How urgent is this case?",
  "criteria": ["Routine", "Urgent", "Emergency"]
}

criteria is required and is an array of 2 to 10 level names, listed in increasing order.

Response shape

{
  "type": "score",
  "score": 1.2,
  "legend": { "0": "Routine", "1": "Urgent", "2": "Emergency" },
  "probabilities": { "0": 0.2, "1": 0.4, "2": 0.4 },
  "confidence": 0.2
}

score — the expected value over the levels, using their 0-based index. In the example above, 0.2*0 + 0.4*1 + 0.4*2 = 1.2.
legend — maps each level index to its name.
probabilities — the distribution over the levels.
confidence — how concentrated the distribution is (see Confidence).

The declared levels are mapped in order to the labels A, B, C, … . The restricted distribution over those labels yields probabilities; score is the probability-weighted mean of the level indices.

---
accTitle: Score produces an expected value over ordered levels
accDescr: Ordered levels map to label tokens; the distribution over the labels is combined with the level indices into the expected score.
---
flowchart LR
  levels["ordered levels<br/>Routine · Urgent · Emergency"]:::success
  labels["label tokens<br/>A · B · C"]:::primary
  probs["level probabilities"]:::accent
  expected["expected value<br/>score = Σ p_i · i"]:::success

  levels --> labels --> probs --> expected

  classDef primary fill:#ede9fe,stroke:#7c3aed,color:#3b0764,stroke-width:1.5px
  classDef accent fill:#dbeafe,stroke:#2563eb,color:#0c4a6e,stroke-width:1.5px
  classDef success fill:#d1fae5,stroke:#059669,color:#064e3b,stroke-width:1.5px

Best practices

Order levels consistently from lowest to highest. The expected value depends on the order.
Use descriptive level names. “Emergency” is clearer than “Level 3”.
Keep levels distinct. Overlapping levels flatten the distribution.
Interpret score together with confidence. A score of 1.2 from a peaked distribution means something different from the same value from a uniform one.

Scripting it

{
  "urgency": {
    "type": "score",
    "instructions": "How urgent is this case?",
    "criteria": ["Routine", "Urgent", "Emergency"]
  }
}

Next steps

Choice and Noul — the other primitives.
Composite scoring — combine scores in code.
Confidence — how certain the model is.

typed-lm