A useful AI postmortem names five things: what users observed, what the system did internally, where controls failed, why the failure passed release gates, and which owners will deliver prevention actions by date.
Why AI Postmortems Need Their Own Structure
Standard incident templates are still useful, but AI failures add new dimensions: model version drift, prompt ambiguity, retrieval quality, and evaluation blind spots. If your template only captures service uptime and stack traces, you lose the decision context that caused the incident.
The goal is not to blame the model. The goal is to document where your product expected deterministic behavior from a probabilistic system and how to reduce that mismatch in the next release.
Copy-Paste AI Incident Postmortem
## AI Incident Postmortem
### Incident Summary
- Date/time detected:
- Detection method (alert, support ticket, manual report):
- Customer-visible impact in one sentence:
- Internal system impact in one sentence:
- Current status:
### Severity and Blast Radius
- Severity level:
- Affected users/accounts/regions:
- Affected features/endpoints:
- Data risk (none / low / medium / high):
- Data exposure or retention concern:
- Compliance or contractual concern:
### Timeline (UTC)
- T0: first known bad behavior
- T1: detection
- T2: mitigation started
- T3: mitigation completed
- T4: full recovery confirmed
### What Failed in the AI Path
- Model and version:
- Prompt/policy segment involved:
- Retrieval/context source involved:
- Tool/function call involved (if any):
- Input pattern or user intent that triggered the failure:
- Example request/response pair, redacted:
- Expected behavior vs actual behavior:
### Why Existing Controls Did Not Catch It
- Pre-release evals that should have failed but passed:
- Runtime guardrails that should have blocked output but did not:
- Monitoring or alerting gaps:
- Human review/process gaps:
### Immediate Mitigation Applied
- Mitigation action(s):
- Rollback or feature flag action:
- Evidence that mitigation worked:
- Residual risk after mitigation:
### Corrective Actions (Prevent Repeat)
- Action 1 / owner / due date / verification signal:
- Action 2 / owner / due date / verification signal:
- Action 3 / owner / due date / verification signal:
### Verification Plan
- How we will verify each corrective action:
- New eval or regression case added:
- Monitoring or alert threshold changed:
- Date for follow-up review:
- Link to regression test plan and eval updates:
### Communication
- Internal channels updated:
- External/customer comms sent:
- Decision log link:
What Teams Usually Miss
If you cannot identify the exact model version and prompt segment involved, you cannot verify the fix. "We changed the prompt" is not an audit trail.
"Improve monitoring" is not an action. Name one owner, one concrete change, and one date.
If the incident does not produce at least one new eval case, your system is still exposed to the same failure mode.
A rollback, prompt edit, or guardrail change is only complete when the team can point to a log, dashboard, eval run, or support queue check showing the bad behavior stopped.
Make Corrective Actions Testable
A prevention item should fail a simple test: could another engineer verify it without asking the incident lead what they meant? If not, tighten the wording until the owner, due date, and evidence are obvious.
- Weak: "Improve prompt safety."
- Better: "Add a refusal eval for unsupported refund policy claims, owned by Support AI, due Friday, passing in CI before release."
- Weak: "Watch this more closely."
- Better: "Alert when fallback rate exceeds 3% for ten minutes, owned by Platform, linked to the model rollback runbook."
Fast Facilitation Prompts
If you run postmortems with AI assistance, use prompts that force specificity. Two examples:
- Timeline prompt: "Summarize this incident timeline in UTC with exactly five milestones: first bad output, detection, mitigation start, mitigation complete, recovery confirmation."
- Action quality prompt: "Review these corrective actions and flag any item that lacks a named owner, due date, or measurable verification step."
- Evidence prompt: "List the logs, eval results, dashboard screenshots, support tickets, or customer confirmations that prove the mitigation worked."
Conclusion
A solid AI postmortem should improve your next release, not just explain your last outage. If your template captures root cause, control failures, and owned follow-up with verification, incidents become expensive lessons once instead of recurring surprises.
Pair this with AI Regression Test Plan Template, Run a Model Fallback Drill Before You Need One, and The AI Decision Log so incident learning feeds directly into future releases.