Choice

A Choice question selects one option from a closed, declared set. Use it for routing, classification and any decision where the answer must be one of a known list.

When should I use Choice?

Routing a request to a department, team or handler.
Classifying a document into a taxonomy.
Selecting one of several candidate functions or skills.
Picking the best passage from a shortlist.

If the decision is boolean, prefer Noul. If it is an ordered intensity, prefer Score.

Request shape

{
  "type": "choice",
  "instructions": "Which department should handle this case?",
  "criteria": {
    "billing": "Double charges and payment errors",
    "logistics": "Damaged, lost, or late shipments",
    "product_support": "Defective-item troubleshooting, replacements, and setup help"
  }
}

criteria is required and is an object mapping each option name to a description. The description may be null when no description is needed.

Response shape

{
  "type": "choice",
  "choice": "logistics",
  "probabilities": { "billing": 0.05, "logistics": 0.9, "product_support": 0.05 },
  "confidence": 0.85
}

choice — the option with the highest restricted-softmax probability.
probabilities — the full distribution over the declared options.
confidence — how concentrated the distribution is (see Confidence).

The options are ordered lexicographically by name and mapped to the spreadsheet labels A, B, C, … . The model reads those label logits at the decision position and applies a temperature-scaled softmax over them.

---
accTitle: Choice scoring
accDescr: Declared options are mapped to label tokens; the restricted softmax produces the winning option and a probability per option.
---
flowchart LR
  options["declared options<br/>billing · logistics · product_support"]:::accent
  labels["label tokens<br/>A · B · C"]:::primary
  logits["label logits at<br/>decision position"]:::neutral
  softmax["restricted softmax"]:::accent
  result["choice + probabilities<br/>+ confidence"]:::success

  options --> labels --> logits --> softmax --> result

  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
  classDef neutral fill:#f4f4f5,stroke:#a1a1aa,color:#18181b,stroke-width:1.5px

Best practices

Make options mutually exclusive. Overlapping options split the probability and reduce confidence.
Give every option a description when the name alone is ambiguous.
Keep the set small. A handful of focused options is more reliable than dozens of near-synonyms. For large taxonomies, use a hierarchy of choices.
Do not add a catch-all unless “other” is a real business outcome; it invites the model to avoid a decision.

Scripting it

curl -s http://127.0.0.1:8080/v1/systemone \
  -H 'Content-Type: application/json' \
  -d @examples/request_mixed.json

Next steps

Score and Noul — the other primitives.
Confidence — act only when the model is sure.
Intent routing — a Choice pattern.

typed-lm