O Gerador de Postmortem Sem Culpa: Transforme Anotações de Timeline Brutas em um Relatório de Causa Raiz
Why this prompt matters
Postmortems that name names or stay vague about root cause do double damage: they discourage engineers from reporting incidents honestly next time, and their action items — things like 'be more careful' or 'improve monitoring' — never actually get assigned or completed, so the same failure mode recurs months later. A blameless report with specific, owned action items is the difference between an incident that makes the system stronger and one that just gets filed away.
What we use it for
An on-call engineer has a payments API outage from 2am with only scattered Slack timestamps and half-remembered details, and needs to turn that into a clear, professional postmortem before Monday's leadership review — without spending two hours writing it from scratch or accidentally writing something that reads like it's blaming a specific teammate.
Prompt
Act as a senior site reliability engineer who runs blameless postmortem reviews and has written hundreds of incident reports that engineering leadership actually reads and acts on. Context: Here are my raw incident notes — Slack messages, timestamps, monitoring alerts, and half-formed recollections, not yet organized: [PASTE YOUR RAW INCIDENT NOTES HERE] Additional context: The affected system was [SERVICE OR SYSTEM AFFECTED]. The customer-facing impact was [DESCRIBE IMPACT, OR 'UNKNOWN' IF NOT YET ASSESSED]. The people involved in the response were [ROLES INVOLVED, e.g. on-call engineer, database admin, incident commander — not names]. Task: 1. Reconstruct a precise, chronological timeline from the raw notes, with timestamps where available. Explicitly flag any gap where you had to infer information or where something is missing, using [NEEDS CONFIRMATION]. 2. Identify the root cause. Distinguish clearly between the triggering event (what set the incident off) and the underlying systemic cause (what allowed it to happen or made it possible) — these are usually different and both matter. 3. Identify contributing factors separately from the root cause — things that made the incident worse, slower to detect, or slower to resolve. 4. Write the entire report in a blameless tone: describe what systems and processes did, not what individual people did wrong. Attribute actions to roles or systems, never to named individuals, even if names appear in my raw notes. 5. Generate specific, assignable action items. Each one must include an owner placeholder and be concrete enough that someone could start work on it immediately without further clarification. 6. Include a genuine "what went well" section based on real details in the notes — not generic filler. Constraints: - Never name or imply blame toward a specific individual, even if my notes do. - Never invent timeline details not supported by the notes — flag uncertainty instead of guessing. - Produce no more than 5 action items. Force prioritization rather than a laundry list. - Write the summary section for an audience that includes non-engineering stakeholders, but keep the timeline technically precise for engineers. Output Format: ## Incident Summary [2-3 sentences: what happened, customer impact, duration] ## Timeline [Chronological, timestamped events] ## Root Cause [Triggering event vs. underlying systemic cause, clearly separated] ## Contributing Factors [Bulleted list] ## What Went Well [Bulleted list] ## Action Items [Numbered list, maximum 5, each with Owner: [PLACEHOLDER] and a concrete deliverable]
Result
Incident Summary
A 47-minute outage in the checkout payments API prevented approximately 12% of transaction attempts from completing between 02:14 and 03:01 UTC. Customers saw a generic error on payment submission; no charges were duplicated or lost, but an estimated 340 checkout attempts failed and required customers to retry.
Timeline
02:14 — Payment API error rate begins climbing, first automated alert fires (P2, not yet paged).
02:19 — Error rate crosses 10% threshold, PagerDuty escalates to P1, on-call engineer acknowledges.
02:23 — On-call engineer confirms payment-service pods are returning 503s; connection pool to the primary database appears exhausted. [NEEDS CONFIRMATION: exact connection pool metric value at this time was not recorded in notes.]
02:31 — Database admin joins the incident channel, identifies a long-running analytics query holding connections open on the primary replica used by payment-service.
02:38 — Long-running query manually terminated; connection pool begins recovering.
02:45 — Error rate drops below 5%, but does not fully recover.
02:52 — Incident commander identifies that payment-service's connection pool size was not reset after the spike, requiring a rolling restart.
03:01 — Rolling restart completes, error rate returns to baseline, incident resolved.
Root Cause
Triggering event: A scheduled analytics query against the production database replica ran significantly longer than expected, holding open a large share of available database connections.
Underlying systemic cause: The analytics workload and the payment-service's transactional workload share the same database replica and the same connection pool ceiling, with no isolation or query timeout enforced on the analytics side.
Contributing Factors
- No automated query timeout exists for scheduled analytics jobs, allowing a single query to hold connections indefinitely.
- Payment-service's connection pool did not automatically shed and reset after the underlying resource pressure cleared, requiring a manual rolling restart to fully recover.
- The initial alert at 02:14 was P2 severity and did not page anyone for 5 minutes, delaying the start of active response.
What Went Well
- The database admin correctly diagnosed the connection pool exhaustion and identified the specific offending query within 8 minutes of joining the incident.
- No data integrity issues occurred — no duplicate or lost transactions despite the connection pressure.
Action Items
- Add a hard query timeout (recommend 60 seconds) for all scheduled analytics jobs running against production replicas. Owner: [DATABASE TEAM LEAD]
- Move analytics workloads to a dedicated read replica, isolated from payment-service's connection pool. Owner: [INFRASTRUCTURE LEAD]
- Adjust payment-service's initial alert severity for error-rate anomalies from P2 to P1 to reduce time-to-page. Owner: [ON-CALL ENGINEERING MANAGER]
- Add automatic connection pool health checks that trigger a self-healing restart when pool exhaustion is detected, removing the need for manual intervention. Owner: [PAYMENT-SERVICE TECH LEAD]
A maioria dos postmortems falha de uma de duas maneiras previsíveis. Ou parecem um relatório de culpa — "o engenheiro de plantão perdeu o alerta" — o que ensina os engenheiros a esconder incidentes em vez de reportá-los honestamente. Ou são tecnicamente precisos, mas geram action items tão vagos que ninguém nunca os conclui: "melhorar o monitoramento", "ter mais cuidado", "adicionar mais testes". Seis meses depois, o mesmo modo de falha se repete.
Este prompt foi construído em torno de algumas decisões de design específicas para evitar ambos os modos de falha.
Por que causa raiz e fatores contribuintes são separados
A maioria das notas de incidente confunde "o que desencadeou isso" com "o que tornou isso possível". Uma query lenta é um gatilho. A ausência de um timeout de query é a causa sistêmica que permitiu que uma query lenta se tornasse uma indisponibilidade. Corrigir apenas o gatilho (matar a query ruim) não resolve o próximo. O prompt força o modelo a separar esses elementos explicitamente, de modo que os action items resultantes ataquem o sistema, e não apenas o sintoma.
Por que linguagem de culpa é banida mesmo quando as notas de origem não são isentas de culpa
Notas brutas de incidente registradas no calor do momento frequentemente contêm nomes e apontamentos de dedo — isso é normal, não um defeito de caráter. O trabalho do prompt é transformar isso em linguagem baseada em papéis e sistemas antes que se torne um documento permanente, lido pela liderança e referenciado por anos. É isso que torna um postmortem seguro para ser escrito honestamente em primeiro lugar: ninguém precisa se preocupar que seu nome apareça em um documento que será encaminhado por aí.
Por que o limite de action items é importante
Uma lista ilimitada de action items é uma lista que ninguém executa. Limitá-la a cinco força uma priorização genuína — o modelo precisa decidir quais correções realmente atacam a causa raiz e os fatores contribuintes, em vez de inflar o relatório com toda ideia de melhoria tangencial que surgiu durante o incidente.
Por que a incerteza é sinalizada em vez de suavizada
Um postmortem que parece confiante sobre detalhes que ninguém confirmou de fato é pior do que um que é honesto sobre suas lacunas. A flag [NEEDS CONFIRMATION] impede que o modelo invente números ou timestamps precisos que não estavam nas notas originais — um modo de falha comum quando uma IA é solicitada a polir uma entrada bagunçada.
Cole suas notas brutas de incidente, preencha os três campos de contexto, e a saída é um documento pronto para uma revisão da liderança — com uma timeline, uma análise de causa raiz real e action items que alguém pode começar a executar hoje.