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Choice: pick one

Choice is Jev’s workhorse: select one option from a set of at most 255 options you define. It fits routing, classification, and picking — any “pick one from a closed set” judgment.

{
"type": "choice",
"instructions": "Which team should handle this email",
"criteria": {
"billing": "Payment, subscription, or invoice issues",
"technical": "Bugs, integrations, API problems",
"sales": "Pricing, plans, account questions"
}
}
  • criteria is a map: keys are the option IDs returned to you; values are the semantic descriptions shown to the model
  • Cap: 255 options (official docs, verified 2026-09-22)
  • Descriptions accept natural language; the official advanced guide notes structured JSON descriptions are also accepted
{
"type": "choice",
"choice": "technical",
"probabilities": { "technical": 0.85, "billing": 0.15, "sales": 0.0 },
"confidence": 0.78
}
Field Meaning Use
choice The selected option ID Drive your business branches directly
probabilities Probability for every option Runner-up fallbacks, multi-way dispatch, weighted policies
confidence Distribution-shape statistic, not accuracy Below threshold → human/fallback (below)
  • Intent routing: dispatch each request to deterministic logic, a specialist model, or a human — the official Intent routing pattern; our walkthrough is in Model routing
  • Hierarchical classification: the official cookbook runs parallel beam search over deep patent, retail, biomedical, and source-code category trees — ask the top level, expand children by probability (via the llms.txt index)
  • Re-ranking: the official cookbook asks one question per query–candidate pair for 40 legal queries, lifting top-1 accuracy from 5% to 18% and top-10 from 38% to 62% (official figures)

Choice pairs naturally with Noul: pick the category, then confirm “does this need human review?” — both in the same request. See Capabilities on parallel questions and the full example in the API reference.