AI content moderation uses automated systems to detect and filter harmful, toxic, or inappropriate content. For LLM applications, this means monitoring outputs for policy violations, hate speech, dangerous advice, and other problematic content.
Content Categories
- Toxicity: Hate speech, harassment, threats
- Harmful: Dangerous advice, self-harm, violence
- Illegal: Instructions for illegal activities
- Adult: Sexual or explicit content
- Policy: Brand-specific violations
Moderation Approaches
- Pre-generation: Filter inputs before LLM
- Post-generation: Classify outputs before delivery
- Guardrails: Block or redact problematic content
- Human review: Escalate edge cases
How do I moderate AI outputs?
Use multiple layers: 1) Model-level safety training, 2) Output classification for toxicity and policy violations, 3) Guardrails to block or redact harmful content, 4) Human review for edge cases. Monitor continuously for new patterns.
Practical example
A forum assistant distinguishes a user reporting abusive language from a user directing abuse at someone.
What to check
Review false alarms and missed cases across languages, quoted material, and contextual edge cases.
Limits and tradeoffs
A content category score is not a complete policy decision and can misread context.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.