Drafting the Postmortem

def generate_postmortem_draft(incident, timeline, rca_result):
    """Generate a postmortem draft from incident data."""
    context = f"""Incident: {incident['title']}
Severity: {incident['severity']}
Duration: {incident['duration']}
Services affected: {', '.join(incident['services'])}

Timeline:
{format_timeline(timeline)}

Root Cause Analysis:
{json.dumps(rca_result, indent=2)}"""

    response = openai.ChatCompletion.create(
        model="gpt-4-turbo",
        temperature=0.3,
        messages=[
            {
                "role": "system",
                "content": """Write a blameless postmortem document.

Structure:
1. Summary — one paragraph describing the incident
2. Impact — user impact, duration, severity
3. Timeline — key events chronologically
4. Root Cause — what caused the incident
5. Contributing Factors — what made the impact worse
6. Resolution — how the incident was resolved
7. Action Items — prioritized list with owners and due dates
8. Lessons Learned — what we learned

RULES:
- Use blameless language. Describe system failures, not human failures.
- Use "we" not "someone should have".
- Focus on what can be improved in the system.
- Action items must be specific, measurable, and assigned."""
            },
            {"role": "user", "content": context}
        ]
    )
    return response["choices"][0]["message"]["content"]

def generate_action_items(rca_result, incident):
    """Generate specific, measurable action items."""
    response = openai.ChatCompletion.create(
        model="gpt-4",
        temperature=0.2,
        messages=[
            {
                "role": "system",
                "content": """Generate action items for this incident.

Each action item must have:
- title: specific, actionable description
- owner: team or role responsible
- priority: P1 (do now), P2 (this week), P3 (this month)
- due_date: specific date
- success_metric: how we know this is done
- type: prevent/detect/mitigate

Focus on systemic improvements, not one-off fixes."""
            },
            {"role": "user", "content": f"RCA: {json.dumps(rca_result)}\nIncident: {incident['title']}"}
        ]
    )
    return json.loads(response["choices"][0]["message"]["content"])