Three Distinct 'Content Filter' Errors in AI APIs Require Different Fixes

AI API content filter errors fall into three distinct categories: an input classifier blocking a request before generation (HTTP 400), an output classifier halting generation mid-way (HTTP 200, finish_reason: content_filter), or the model itself issuing a polite refusal with no filter involved (HTTP 200, finish_reason: stop). The third case — a trained model refusal — is the most frequently misdiagnosed, causing teams to waste time filing policy appeals or rewriting prompts against a filter that never actually fired. Developers can distinguish all three by checking the HTTP status code and finish_reason field, along with per-category annotations where providers supply them. Safety classifiers are deliberately tuned to favour false positives over false negatives, which explains why legitimate use cases in medical, security, news, and support contexts frequently trigger them. Logging provider-returned category annotations rather than hard-coding category names is recommended, as taxonomies and severity scales vary across providers and API versions.