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A Noul question asks a yes/no question and returns a single calibrated probability between 0 and 1 - the probability the answer is “yes.” There’s no separate label field; you threshold the probability yourself to make a binary decision. Use a Noul when the probability itself is the useful signal, not just a boolean. “Does this request qualify for a refund?” might return 0.999 (clearly yes) or 0.52 (genuinely ambiguous) - a plain true/false would hide that difference.
Noul vs. Score: a Noul at 0.5 means maximum uncertainty between yes and no - it doesn’t express degree. If you need to measure degree (urgency, severity, frustration), use Score with defined levels instead.

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Request

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string
required
Always "noul".
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string
required
The yes/no question to evaluate, in natural language.
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object
Optional labels for the outcomes, with true and false description keys, e.g. {"true": "Qualifies", "false": "Does not qualify"}. Omit it to use "Yes" and "No".

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Answer object

The matching entry inside answers is:
  • noul - the probability the answer is “yes.” 0.999 is a strong qualifying signal.
  • confidence - |2 × noul − 1|, from 0 (maximum uncertainty, noul = 0.5) to 1 (fully certain, noul = 0 or 1). See confidence and uncertainty for how to act on it.

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Using the result

Tune the thresholds (0.8 / 0.2 above) on real data for your use case rather than assuming they transfer - probability and confidence are calibrated per model, not guaranteed to map onto any particular business tolerance for ambiguity.