content moderation

Moderation is a multi-stage classification problem with brutal tradeoffs: false negatives cause real harm, false positives censor legitimate users, and the adversary adapts. The signal is the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. Here is the design.

Truth is not a label you can train on cheaply, and adversaries adapt the moment you ship. A strong answer fuses content, graph, and behavioral signals, puts humans in the loop where precision matters, and treats adversarial drift as a permanent operating condition, not a one-time training problem.

Moderating billions of items a day with an LLM on every one is too slow and too expensive. Learn the tiered funnel (cheap classifier then LLM then human), how to tune thresholds for precision versus recall, and how to keep up with adversaries.

Toxicity detection looks like vanilla text classification and is anything but: context flips labels, adversaries evolve weekly, and naive models flag dialects as hate. The signal is naming those failure modes and designing the human-in-the-loop system around them.