Every production LLM app eats false-positive refusals. A user asks something perfectly fine, the safety filter trips, the model emits two sentences of "I can't help with that," and your UI shows a wall. Do that a few times and the user leaves.
We've measured this on HoneyChat — Telegram-native AI companion, ~300 DAU, 17 languages. Across a normal day, somewhere between 2% and 8% of model calls land in a refusal or finish_reason="content_filter" state. Most of those are not actually problematic content — they're the model being twitchy about edge phrasing, polysemous words, or roleplay framing. The pattern below recovers about 70% of them.
HoneyChat LLM routing at a glance (core/llm.py, plan-gated via OpenRouter):
Tier(s)
Pace
