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Safety
Content Filtering
Definition
Content filtering in AI systems automatically screens LLM inputs and outputs to block or flag harmful, inappropriate, or policy-violating content before it reaches users.
Why It Matters
LLMs can generate toxic, harmful, or inappropriate content even when prompted innocently. Content filtering provides a safety net that catches problematic outputs regardless of why they were generated. It’s especially important for consumer-facing applications where users may not expect harmful content.
Types of Filtering
Input Filtering:
- Block known harmful queries
- Detect prompt injection attempts
- Filter personal information
Output Filtering:
- Toxicity detection
- Hate speech identification
- Sexual content screening
- Violence detection
- Custom policy enforcement
Implementation Options
API-based:
- OpenAI Moderation API (free)
- Azure Content Safety
- Perspective API (Google)
- Commercial moderation services
Self-hosted:
- Llama Guard
- Detoxify
- Custom classifiers
Best Practices
Filter both inputs AND outputs. Log filtered content for analysis. Allow appeals or overrides for edge cases. Update filters as new patterns emerge. Balance false positives against safety.