Root Cause Analysis
# 🐞 Root Cause Analysis Playbook
## 🎯 Purpose
Use this playbook to systematically investigate bugs, production incidents, failures, regressions, performance issues, and unexpected system behavior.
Rather than jumping to conclusions or proposing immediate fixes, this playbook helps identify the true root cause, assess impact, and recommend corrective and preventive actions.
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# ✅ When to Use
Use this playbook when you want to:
* Investigate a production incident
* Debug a difficult bug
* Analyze performance degradation
* Understand intermittent failures
* Investigate application crashes
* Perform post-incident reviews
* Identify regression causes
* Prepare an RCA document
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# 📥 Inputs Required
Provide as much context as possible.
## Problem Summary
Describe:
* What happened?
* What was expected?
* What actually occurred?
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## Timeline
Include:
* When did the issue begin?
* Was it after a deployment?
* Was it intermittent or consistent?
* Has it happened before?
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## Environment
Examples:
* Development
* QA
* Staging
* Production
Include:
* OS
* Browser
* Device
* Cloud provider
* Region (if applicable)
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## Technical Context
Provide:
* Technology stack
* Architecture overview
* Recent deployments
* Recent configuration changes
* Relevant code snippets
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## Evidence
Include:
* Error logs
* Stack traces
* Screenshots
* Monitoring graphs
* Metrics
* Console output
* Network traces
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## Reproduction Steps
If known:
* Steps to reproduce
* Frequency
* Preconditions
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# 🚀 Copy-Paste Promptwhen to use it
Community prompt sourced from the open-source GitHub repo aksconnect/engineering-ai-playbook (MIT). A "Root Cause Analysis" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.
tags
businesscommunitygeneral
source
aksconnect/engineering-ai-playbook · MIT