understanding-mistakes

To write a postmortem for AI behavior deviations, use a guided framework to identify what happened, why it occurred, and how to prevent recurrence. This process generates a structured learning document with root-cause analysis and actionable recommendations.

The best way to document root-cause analysis for unexpected AI outputs is to generate a standardized learning file in docs/learnings. This captures the deviation details and aggregates insights to build a knowledge base of frequent failure modes.

You create a standardized learning document by running a postmortem analysis on the latest deviation and saving it as a YYYY-MM-DD formatted file. This aggregates recurring issue patterns into a traceable knowledge base for consistent improvements.

Yes, you can apply this postmortem framework across engineering, product, and content workflows. It is designed to handle any scenario where AI outputs diverge from expectations, enabling consistent root-cause analysis regardless of the specific workflow.

You need a structured postmortem analysis for AI safety whenever outputs diverge from expectations and require honest reflection. It guides you through identifying the deviation and formulating concrete learning to prevent future recurrence.

No, this postmortem process requires no specific dependencies to generate documentation. It operates independently to analyze deviations and create structured learning files under docs/learnings for traceable improvements.