How Batch LLM Triage Can Route Large Game Moderation Queues Efficiently

A developer experiment explores using batch large language model classification to manage high volumes of user-generated game reports more efficiently than full manual review. The approach assigns labels such as 'allow', 'action', or 'review' to each report, routing only borderline or high-severity cases to human moderators. Token counting before submission is recommended to track costs and detect prompt growth as moderation policies evolve. Two routing gates are proposed: severe labels like credible threats always reach a human, while ordinary labels enter review only when model confidence falls within an uncertainty band. The author cautions that no latency, accuracy, or cost savings were measured, and that thresholds will likely need tuning based on game type, audience, and language mix.