Product

Catch Invalid AI Labels

The model answered "Complaints" — your set says "Complaint". One character of drift, one broken dashboard. Caught before it counts.

Open in AI Output Validator

Overview

Invalid labels poison aggregations silently: "Complaints" instead of "Complaint" creates a new category with a count of one, dashboards fragment, and nobody notices until the quarterly review. This setup validates a feedback classification that returned a near-miss label outside the defined set — the plural drift that exact string matching downstream will treat as a brand-new category. The validator flags it as a fail with the full allowed set in the repair, and the fix costs one message instead of one data-cleaning sprint.

How to use this resource

Validate at the gate

Between the model and the database — the only place a label fix is one message cheap.
Watch for near-misses

"Complaints" vs "Complaint" is the signature drift: too close for a human to flag, far enough to fragment data.
Repair with the full set

The repair prompt lists every allowed label — the model picks the right one with the set in view.

Why This Works

Set-membership validation matches how aggregation actually breaks
Near-miss labels are exactly what human review approves without noticing
A one-message repair beats retroactive data cleaning every time

Best for

Feedback pipelines aggregating labels over time
Dashboards that fragment on novel label strings
Anyone who has run SELECT DISTINCT label and wept

Not for

Improving the label definitions so drift stops — that's the Data Classification Prompt
Multi-label ordering checks — this validates set membership and format

Use cases

Gating labels before they enter counts and dashboards
Catching plural/singular and synonym drift
Keeping category sets closed in long-running pipelines

FAQ

How does the repair prompt fix a label like "Complaints" that's almost right?

It flags the value in PROBLEMS FOUND as "Invalid label: \"Complaints\" — not in the defined label set", and the Fix line lists the full allowed set (Praise, Complaint, Feature Request, Bug Report, Question) so the model re-picks with every option in view. The ai-output-validator surfaces the drift and generates this repair; you run it in your assistant and confirm the returned label before it hits your database.

Will running the repair change other parts of my classification output?

The OUTPUT RULES constrain it to fix only the listed problems and keep everything already correct unchanged, with no explanations or code fences in the reply. It checks set membership and format, not multi-label ordering, per the notFor. The validator surfaces issues but doesn't guarantee it catches every possible drift, so a human still owns the final gate before data enters counts.

Customize This Resource

Opens this setup in AI Output Validator. Validate to see the score, every issue found, and the repair prompt to send back to the model.

Open in AI Output Validator

Prompt Template

Copy it as-is, or use Open in AI Output Validator to load it pre-filled and customize it with your own context.

REPAIR TASK
Your previous response did not follow the required format. Fix the problems listed below and return only the corrected output.

ORIGINAL REQUIREMENT
Validate feedback categorization output against the defined categories.
Expected: exactly one of the labels, exactly as defined: Praise, Complaint, Feature Request, Bug Report, Question.

PROBLEMS FOUND
- Invalid label: "Complaints" — not in the defined label set. Fix: choose exactly one of: Praise, Complaint, Feature Request, Bug Report, Question.

OUTPUT RULES
- Return only the corrected classification — no explanations, no commentary, no code fences.
- Keep everything that was already correct unchanged.
- Fix only the problems listed above — do not change anything else.

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