Postmortem Assumptions and Unknowns Extractor Prompt
Read a postmortem draft and surface every unstated assumption and open unknown that is being treated as settled fact, so the root-cause analysis and action items don't quietly rest on unverified claims.
- Target user
- Incident commanders, SREs, and postmortem reviewers hardening their analysis
- Difficulty
- Intermediate
- Tools
- Claude, ChatGPT, Cursor
The prompt
You are a senior SRE reviewing a postmortem draft for hidden assumptions and unverified claims. Your goal is to make the analysis honest about what is known versus believed, without blaming the authors or responders.
I will paste:
[POSTMORTEM_DRAFT] — the writeup, including timeline, root-cause narrative, and action items.
[KNOWN_EVIDENCE] — optional: logs, metrics, or links that back specific claims.
Do this:
1. Extract ASSERTED FACTS: pull each causal or factual claim the draft makes ("the retry storm caused the DB to fall over").
2. For each, classify as SUPPORTED (evidence cited), PLAUSIBLE-BUT-UNVERIFIED, or ASSUMPTION (stated as fact with no backing).
3. List HIDDEN ASSUMPTIONS: the unstated beliefs the narrative depends on (e.g., "the deploy and the outage are related because they were close in time").
4. List OPEN UNKNOWNS: questions the draft treats as settled but hasn't actually answered, and what evidence would resolve each.
5. Flag CONFOUNDERS: alternative explanations the draft dismisses or never considers.
6. Recommend the smallest set of VERIFICATION STEPS that would move the biggest assumptions to SUPPORTED.
Output as Markdown: a claims table (claim / classification / evidence-needed) plus the hidden-assumptions, open-unknowns, and confounders lists. Stay blameless: you are testing the analysis, not the analysts. Mark anything you infer beyond the text as [ESTIMATE]. A human review owner decides which assumptions must be verified before the postmortem is finalized; you only surface them.
Run this prompt with AI
Test it, get an AI-improved version, or compare models — live in the Prompt Workspace. No copy-paste.
Why this prompt works
Postmortems fail silently when a plausible story hardens into an accepted root cause without anyone checking whether the evidence actually supports it. The classic pattern is temporal correlation masquerading as causation: a deploy went out twelve minutes before the outage, so the deploy caused the outage, and every action item flows from that assumption. If the real cause was an unrelated dependency degrading in the background, the team ships fixes that prevent a failure that will never recur while leaving the actual fault untouched. This prompt is a dedicated adversary to that pattern, pulling apart what the draft knows from what it merely believes.
The three-way classification of asserted facts is deliberately more forgiving than a simple true/false split. Most claims in a good postmortem are PLAUSIBLE-BUT-UNVERIFIED rather than outright assumptions, and that is fine, so long as the team knows which ones they are and chooses consciously whether to verify them. Forcing every causal claim into the table makes the confidence level explicit instead of leaving it implied by fluent prose. The hidden-assumptions and confounders sections attack the harder problem: the beliefs that never appear as sentences at all, and the alternative explanations the narrative quietly excluded. These are exactly the things authors cannot see in their own draft, because they are the frame through which they wrote it.
Blameless framing is essential because this prompt is, structurally, a critique. Reviewing a colleague’s causal analysis and finding it rests on assumptions can read as an accusation of sloppiness if the language isn’t careful. By insisting the target is the analysis and not the analysts, the prompt keeps the exercise collaborative: everyone benefits from a postmortem whose conclusions survive scrutiny, and nobody should feel exposed for having written a first draft that made reasonable leaps. Good postmortems are iterative, and surfacing assumptions is part of the iteration, not a mark against the author.
The strongest guardrail is the instruction not to resolve the unknowns. An eager model will happily fill gaps with invented evidence or confidently pick a root cause, which would defeat the entire purpose by replacing the draft’s unverified certainty with the model’s unverified certainty. Requiring [ESTIMATE] markers and handing the verification decisions to a human review owner keeps the tool in its proper role: a lens that reveals where the analysis is standing on unverified ground, leaving the actual verification to people with access to the real evidence.
Related prompts
-
Postmortem QA Reviewer Prompt
Run a quality pass over a finished postmortem draft to flag missing sections, claims unsupported by the timeline, and unaddressed single points of failure — before it goes to review.
-
Postmortem to Game-Day Scenario Generator Prompt
Convert a real incident postmortem into a runnable game-day or chaos-engineering exercise so you can prove the fixes actually work under realistic failure conditions instead of assuming they do.
-
Postmortem Incident Commander Decision Review Prompt
Review the key decisions an incident commander made during a response and evaluate each for local rationality given the information available at the time — blamelessly, focused on decision-support tooling and process.
-
Postmortem On-Call Handoff Quality Analyzer Prompt
Analyze the on-call and shift handoffs that happened during a long-running incident to find where context was lost at the handoff boundary and what handoff artifact would have prevented it.
More Post Mortems with AI prompts & error guides
Browse every Post Mortems with AI prompt and troubleshooting guide in one place.
Reading prompts? Get all 500 in one free PDF
500 battle-tested, copy-paste AI prompts engineered by a senior systems engineer — every one with fill-in placeholders and safety/back-out notes. Drop your email and it's yours.
- 500 prompts: Linux · Kubernetes · Terraform · OpenStack · GitLab · Docker · Monitoring · Incident Response
- Instant PDF download — yours free, forever
- Plus one practical AI-workflow email a week (no spam)
Single opt-in · unsubscribe anytime · no spam.