Score on an ordered rubric with GLiDE - Fastino Labs

A general-purpose LLM scoring prompt asks the model to generate a number or return structured output. With GLiDE, you provide an ordered rubric and receive a probability distribution across those levels. Use this pattern for urgency, severity, quality, sentiment intensity, risk, and other ordered judgments.

Score support urgency

import os

import requests

ticket_text = (
    "Our production API is returning 500 errors on every request. "
    "All customers are affected. Revenue impact is approximately $12,000 per hour. "
    "Started 15 minutes ago."
)

response = requests.post(
    "https://api.fastino.ai/v1/systemone",
    headers={"X-API-Key": os.environ["FASTINO_API_KEY"]},
    json={
        "model": "fastino/GLiDE",
        "state": ticket_text,
        "questions": {
            "urgency": {
                "type": "score",
                "instructions": "How urgent is this support request?",
                "criteria": [
                    "Can wait: no immediate business impact",
                    "Should handle today: minor inconvenience",
                    "Time-sensitive: noticeable customer impact, needs attention within hours",
                    "Critical: system failure affecting customers or revenue",
                ],
            }
        },
    },
    timeout=300,
)
response.raise_for_status()

answer = response.json()["answers"]["urgency"]
level = answer["score"]  # Discrete winning level, such as 3.
continuous_score = answer["expected_level"]  # Probability-weighted position, such as 2.999.
confidence = answer["confidence"]
distribution = answer["probabilities"]  # String keys: {"0": ..., "1": ..., "2": ..., "3": ...}

score is the integer index of the most likely level. Use expected_level when you need a continuous value between levels. The response also includes legend, which maps the string index keys back to the descriptions you supplied.

Do not treat score as continuous. For example, 2.999 belongs in expected_level; score would be the winning integer level, such as 3.

Turn the score into an action

if level == 3 and confidence >= 0.7:
    action = "page_on_call"
elif continuous_score >= 2.0:
    action = "escalate"
else:
    action = "standard_queue"

Tune thresholds on labeled examples from your application. confidence measures separation between the two most likely levels; it is not the same value as the continuous score.

What changes from LLM scoring

The output is constrained to your ordered rubric; there is no generated number to validate.
You receive both the winning level and a probability-weighted continuous position.
The complete distribution shows whether the input sits cleanly on one level or between levels.
Your criteria remain in the response legend, making score interpretation explicit.

See GLiDE Inference for authentication, limits, errors, and the complete response contract.

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