The AI Incident Report Template I Actually Use for Wrong Answers and Tool Failures

Most AI incidents are documented too late and too vaguely. The team remembers the frustration, but not the evidence. So a week later the postmortem sounds like this: "The model got weird." "Retrieval seemed off." "Tool calling was flaky." "We think the prompt change may have caused it." That kind of report is not useful. If you want incidents to improve the system instead of just creating a document, the write-up has to force clarity. This is the lightweight template I actually like for production AI incidents. What makes AI incidents annoying AI incidents usually cross more than one layer: model behavior prompt or policy changes retrieval quality tool execution downstream parsing logging gaps That is why generic incident templates often fail here. They capture "what happened" but not the behavioral context needed to debug probabilistic systems. You do not need a giant framework. You do need a report that makes the team answer the right questions. The template This is the copy-paste ve