> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fastino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Use https://docs.fastino.ai/openapi.json as the source of truth for customer-facing routes. For GLiDE decision inference, call POST https://api.fastino.ai/v1/systemone with model fastino/glide. Do not infer undocumented routes. Read API keys from FASTINO_API_KEY and never embed credentials in code, logs, or reports.

# Noul — GLiDE's yes/no decision primitive

> Ask GLiDE a yes/no question and get back a calibrated probability instead of a label, so you can threshold on confidence instead of guessing.

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.

<Tip>
  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](/concepts/glide-score) with defined levels instead.
</Tip>

## Request

<ParamField body="type" type="string" required>
  Always `"noul"`.
</ParamField>

<ParamField body="instructions" type="string" required>
  The yes/no question to evaluate, in natural language.
</ParamField>

<ParamField body="criteria" type="object" required>
  An object with `true` and `false` description keys, e.g. `{"true": "Qualifies", "false": "Does not qualify"}`.
</ParamField>

<CodeGroup>
  ```bash cURL theme={null}
  curl -s https://api.fastino.ai/v1/systemone \
    -H "X-API-Key: YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "fastino/glide",
      "state": "Refund request: the receipt is attached, the purchase was 10 days ago, and refunds are allowed within 30 days.",
      "questions": {
        "refund_allowed": {
          "type": "noul",
          "instructions": "Does this request qualify for a refund?",
          "criteria": { "true": "Qualifies", "false": "Does not qualify" }
        }
      }
    }'
  ```
</CodeGroup>

## Response

```json theme={null}
{
  "refund_allowed": {
    "type": "noul",
    "noul": 0.99767683794902,
    "confidence": 0.9953536758980399
  }
}
```

* `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](/concepts/decision-models#confidence) for how to act on it.

## Using the result

```python theme={null}
answer = response["answers"]["refund_allowed"]

if answer["noul"] > 0.8:
    action = "auto_approve"
elif answer["noul"] < 0.2:
    action = "auto_deny"
else:
    action = "human_review"  # genuinely ambiguous — don't force a threshold here
```

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.

## Related

* [Choice](/concepts/glide-choice) — pick one of several named options
* [Score](/concepts/glide-score) — rate on an ordered scale
* [Decision Models](/concepts/decision-models) — primitives overview, limits, and the full request contract
* [GLiDE](/concepts/glide) — what GLiDE is and when to use it