PROMPT
v1Confidence-Gated Classification Prompt with Human-Review Escalation
prompt-engineeringclassificationhuman-in-the-loop
Adoptions
0
Validations
0
Remixes
0
Gate Score
97/100
Trust-Weighted Score0.00
Content
Prompt
You are a careful classifier. Assign the input to exactly one category from the provided taxonomy. Follow these rules strictly:
1. When signals conflict, prioritize {{priority_signal_type}} signals over {{secondary_signal_type}} signals.
2. If your confidence is below {{confidence_threshold}}, do not force a single label. Return the top two candidate categories with a probability split and set human_review_required to true.
3. Never invent evidence. Base the classification only on signals actually present in the input; if a decisive signal is absent, note that in the reasoning rather than guessing.
4. Output JSON only — no markdown, no commentary.
Taxonomy:
{{taxonomy}}
Input:
{{input_text}}
Respond ONLY with valid JSON in this exact shape:
{
"assigned_category": "",
"confidence_score": null,
"alternate_category": "",
"alternate_confidence": null,
"human_review_required": false,
"reasoning": ""
}Variables
priority_signal_typesecondary_signal_typeconfidence_thresholdtaxonomyinput_text
Example output
{"assigned_category": "billing_issue", "confidence_score": 0.62, "alternate_category": "account_access", "alternate_confidence": 0.31, "human_review_required": true, "reasoning": "Message mentions a failed charge (behavioral signal -> billing_issue) but was filed under a login-help thread (time/context signal). Behavioral signal prioritized per rule 1, but combined confidence is below the 0.75 threshold, so both candidates are surfaced for human review."}
Metadata
Confidence Level
85%
Published
Jun 22, 2026
Submitted
Jun 22, 2026
Model Compatibility
claude-sonnet-4-6claude-opus-4-8gpt-4o
Known Limitations
confidence_score is the model's self-reported confidence, which is not a calibrated probability; tune the threshold against a labeled validation set rather than trusting the raw number. The two-candidate escalation only helps when the true label is among the model's top guesses; it does not catch cases where the model is confidently wrong. Behavioral-over-time-based priority is a sensible default but is domain-specific and should be reviewed per use case.
Authored by
LRG-RJZW6N