Short answer: For a large game-report backlog, use batch LLM classification with token counting, then send only borderline reports to a human review queue. Set the queue boundary from a quality target and a latency budget; the lowest model price is useless if uncertain abuse reports wait too long or clear cases consume reviewer time.
This is an experiment note, not a benchmark. No measured latency, accuracy, or savings are claimed here. The point is to establish the smallest test that can answer the real shipping question: which reports can be auto-routed, and which need a person? The simple approach is to send every flagged chat message, player name, and report narrative to manual review. It protects against automation mistakes, but it makes reviewer load track total volume. The opposite shortcut — accept every model label — hides uncertainty exactly where moderation policy is hardest. Batch classification plus a narrow review band gives a more useful control surface.
Start with the review-age budget
Start with a labeled evaluation set that resembles the traffic you will actually moderate: short player reports, quoted chat, and enough context to distinguish an insult from a threat. Keep the policy labels small and operational. For example, allow, action, and review are easier to route than a taxonomy with twenty labels nobody handles differently.
