When the LLM Refuses: A Fallback Chain That Salvages Most Refusals

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.