AI Content Filter: The Practitioner's Playbook for Killing Low-Quality LLM Slop at Scale

Short summary

A practical guide to detecting and filtering AI-generated low-quality content at scale using lightweight heuristics. Stack perplexity scoring, type-token ratio, sentence variance, and transition phrase density into a composite score; auto-flag anything >0.6 for human review or >0.8 for auto-hold. Implement a three-tier moderation queue and monthly retraining loop; weight account history heavily to avoid false positives.

• •Use lightweight heuristics (perplexity, TTR, sentence variance, transition density) for 60–70% baseline detection accuracy
• •Build a three-tier moderation queue: auto-pass (<0.4), soft-hold (0.4–0.75), hard-hold (>0.75) with human review timelines
• •Retrain monthly on mod feedback, weight account history heavily, and create appeal flows to handle legitimate edge cases

Generated with AI, which can make mistakes.