USE-CASES

Content moderation classification with a review queue

Turn a written text policy into a repeatable triage step. The classifier helps sort the queue; your team remains responsible for enforcement and appeals.

JEV / DECISION LABYOUR RULES. YOUR LABELS.
Remove passwords, payment details, and unnecessary personal information. 85%
OUTPUT / JSON

Your decision

Run a decision or open the labeled sample to see the result here.

A high score is not a guarantee. Keep a person in control of consequential actions.

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A customer message enters your label rules, becomes a typed category, and reaches a queue with a review path.
A customer message enters your label rules, becomes a typed category, and reaches a queue with a review path.

Write the rule before testing the model

Specify the behavior that violates your policy, permitted exceptions, and what counts as insufficient context. Avoid a single vague instruction such as “detect bad content.” Quotation, reporting abuse, and satire can change the meaning of the same phrase.

Use three outcomes

Allow covers clearly permitted text. Review covers ambiguity and missing context. Block should be reserved for clear violations supported by your policy and evaluation results. A confidence threshold adds a second review condition; it does not replace the policy.

This workflow accepts text. Do not use it as an image, audio, or video moderation system.

Request contract · OpenAI Moderation API vs custom policy rules

Audit errors in both directions

False positives silence legitimate users; false negatives expose others to harmful content. Review both, compare performance across supported languages, and provide an appeal path. Do not represent a model score as a legal finding or certainty.

Human-in-the-loop decisions

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    Describe the workflow you want to classify, the volume you expect, and the outcome you need. We reply by email.

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