Lilly Tech Systems
Designing AI Content Moderation Systems
Build production-grade trust & safety systems from scratch. This course covers the complete moderation architecture stack — from text toxicity detection and image/video analysis to policy engines, human review pipelines, and real-time scaling. Every lesson includes production code, architecture patterns, and techniques used by platforms moderating billions of pieces of content daily.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Content Moderation Architecture
Why automated moderation matters, moderation pipeline overview, types of harmful content, human-in-the-loop design, and real platform examples.
2. Text Content Moderation
Toxicity detection with Perspective API and OpenAI, custom classifiers, multilingual moderation, context-aware detection, and adversarial text handling.
3. Image & Video Moderation
NSFW detection, violence detection, OCR for text-in-images, video frame sampling, deepfake detection basics, and cloud vision API integration.
4. Policy Engine Design
Rule-based vs ML-based policies, policy versioning, A/B testing, severity scoring, action mapping, and escalation workflows.
5. Human Review Pipeline
Queue management, reviewer assignment algorithms, quality assurance, reviewer wellness, SLA management, and review queue implementation.
6. Real-Time Moderation at Scale
Pre-publish vs post-publish moderation, latency requirements, distributed processing, batch vs streaming, and cost optimization.
7. Best Practices & Checklist
Moderation system checklist, precision/recall metrics, false positive impact analysis, appeals process design, and comprehensive FAQ.