Content Moderation
Filter and classify user-generated content
Automatically classify user-generated content by policy violation type, severity, and recommended action at massive scale. Handles nuanced cases that keyword filters miss while maintaining consistent enforcement across all content types.
Gemini 1.5 Flash
for 10,000K tokens/month · 90% input / 10% output
WHY THIS MODEL
Gemini 1.5 Flash is the benchmark for high-throughput automation: sub-second latency and low cost per token make it the only economically viable choice for pipelines processing tens of millions of tokens daily. Its extraction accuracy is sufficient for most classification and moderation tasks.
ALTERNATIVE MODELS
IMPLEMENTATION TIPS
- 1
Define your policy taxonomy as an enum in the system prompt with 1–2 example violations per category — precise policy definitions eliminate the ambiguity that causes inconsistent enforcement across the model's outputs.
- 2
Use a tiered confidence approach: high-confidence violations are actioned automatically, medium-confidence items are queued for human review, and low-confidence items pass — this human-in-the-loop design maintains precision at scale.
- 3
Run a monthly calibration session: sample 200 decisions, have human reviewers re-score them, and update your system prompt with patterns where the model consistently disagrees with human judgment.