post-mortem-learning

To conduct a root cause analysis, you input structured failure data and QA reports to identify contributing factors and generate actionable lessons. The process reconstructs end-to-end failure narratives to drive systemic process improvement.

To generate raw lessons from a project failure, you need structured inputs including failure results, QA reports, context artefacts, memory decisions, and rule applications. These inputs allow the system to reconstruct end-to-end failure narratives.

Yes, failure analysis produces structured outputs including improvement candidates specifically for backlog refinement. It also generates root cause analysis, contributing factors, raw lessons, and failure scenarios from your failure data.

The best way to analyze pattern-driven problems across teams is to apply a structured post-mortem review to significant delivery failures and QA missteps. This identifies systemic root causes and generates concrete process improvements.

You should trigger a post-mortem review after a significant QA misstep or delivery failure occurs. This allows you to document contributing factors, extract raw lessons, and formulate improvement candidates to prevent recurrence.