MLOpsProduction Incident ResponseIntermediateSingle prompt

Incident Post-Mortem AI Prompt

This prompt writes a blameless post-mortem for an ML incident, focusing on timeline, causes, impact, lessons, and tracked action items. It is useful for organizations that want learning-oriented incident reviews rather than one-off summaries.

Prompt text
Write a blameless post-mortem for this ML model incident.

Incident summary: {{incident_summary}}
Model affected: {{model_name}}
Incident duration: {{duration}}
Business impact: {{business_impact}}

Blameless post-mortem principles:
- The goal is to learn and prevent recurrence, not to assign blame
- People acted with good intentions given the information they had at the time
- Focus on system and process failures, not individual failures

1. Incident summary:
   - What happened? (2–3 sentences, suitable for a non-technical audience)
   - When did it start? When was it detected? When was it resolved?
   - Who was involved in the response?

2. Timeline (chronological):
   - [timestamp] — Event description
   - Include: first symptom, alert triggered, incident declared, triage started, root cause identified, mitigation applied, full resolution

3. Root cause analysis:
   - What was the immediate cause? (What triggered the incident?)
   - What were the contributing causes? (Five Whys or similar)
   - What allowed this to happen? (System design, monitoring gap, process gap)

4. Impact assessment:
   - User impact: how many users or requests were affected?
   - Business impact: estimated revenue impact, SLA violations, customer complaints
   - Data impact: any data corruption or loss?

5. What went well:
   - What detection, response, or mitigation actions worked effectively?

6. What went wrong:
   - What slowed detection, diagnosis, or resolution?

7. Action items (the most important section):
   - For each: what will be done, who owns it, and by when
   - Categorize: immediate fix, monitoring improvement, process improvement, systemic fix
   - All action items must be in a tracking system within 24 hours of the post-mortem

Return: complete blameless post-mortem document.

When to use this prompt

Use case 01

when an ML incident needs formal retrospective documentation

Use case 02

when root cause and contributing factors should be recorded without blame

Use case 03

when action items need owners and deadlines

Use case 04

when post-incident communication should work for both technical and non-technical readers

What the AI should return

A complete blameless ML incident post-mortem document with summary, timeline, root cause analysis, impact, lessons, and action items.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Production Incident Response.

Frequently asked questions

What does the Incident Post-Mortem prompt do?+

It gives you a structured production incident response starting point for mlops work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for mlops workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Incident Post-Mortem is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Emergency Rollback Procedure, Incident Classification Matrix, Incident Response Chain.