How do content moderation classifiers work in LLM apps, and how do you set their thresholds?

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Moderation classifiers score text (or images) for categories like hate, harassment, sexual content, violence, self-harm and illegal activity. Options include provider moderation endpoints, open safety models (e.g. Llama Guard–style classifiers that take a policy taxonomy), and custom fine-tuned classifiers. Thresholds trade false positives against false negatives per category. Set them with a labelled dataset, considering the severity of each harm and the product context, and review borderline cases with humans.

Threshold setting process

Label a dataset per category with realistic positives, negatives and borderline cases from your domain (e.g. medical discussions shouldn't be flagged as violence).
Plot precision–recall across thresholds per category.
Choose per category by harm severity:

Category	Priority	Threshold choice
Child sexual content	Zero tolerance	Very high recall; block + escalate
Self-harm	High recall + supportive response	Route to a safe response, not just a block
Profanity in casual chat	Low severity	High precision; avoid over-blocking

4. Actions per score band: allow / warn / soft-block with rephrase / hard-block / human review.

5. Monitor block rates, appeals and missed incidents; recalibrate periodically.

Context matters: the same words are fine in a medical, legal or security-education product and not in a kids' app. Custom policies or prompts for policy-following classifiers handle this.

Multilingual: test classifiers on Hindi, Hinglish and regional languages. Performance often drops outside English.

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How do content moderation classifiers work in LLM apps, and how do you set their thresholds?

Threshold setting process

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