Archive/PROMPT/LRG-CONTRIB-H8N468YW
PROMPT
v1

Confidence-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

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