مولد تقرير ما بعد الحادث الخالي من اللوم: حوّل ملاحظات الجدول الزمني الأولية إلى تقرير السبب الجذري
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]
تفشل معظم تقارير ما بعد الحادث بإحدى طريقتين متوقعتين. إما أنها تبدو وكأنها تقرير اتهام - "فات المهندس المناوب التنبيه" - مما يعلّم المهندسين إخفاء الحوادث بدلاً من الإبلاغ عنها بصدق. أو أنها دقيقة تقنياً لكنها تنتج بنود عمل غامضة لدرجة أن لا أحد يكملها أبداً: "تحسين المراقبة"، "توخي الحذر أكثر"، "إضافة المزيد من الاختبارات". بعد ستة أشهر، يعود نمط الفشل نفسه.
تم بناء هذا الـ Prompt حول مجموعة من القرارات التصميمية المحددة التي تهدف إلى تجنب كلا نمطي الفشل.
لماذا يتم فصل السبب الجذري والعوامل المساهمة
معظم ملاحظات الحوادث تخلط بين "ما الذي أثار هذا" و"ما الذي جعله ممكناً". استعلام بطيء هو المشغّل. غياب مهلة زمنية للاستعلام هو السبب النظامي الذي جعل الاستعلام البطيء يؤدي إلى انقطاع الخدمة. إصلاح المشغّل فقط (قتل الاستعلام السيئ الواحد) لا يفيد في الاستعلام التالي. يجبر الـ Prompt النموذج على فصل هذه العناصر صراحة، بحيث تعالج بنود العمل الناتجة النظام وليس مجرد العرض.
لماذا يتم حظر لغة اللوم حتى عندما تكون الملاحظات المصدرية غير خالية من اللوم
ملاحظات الحوادث الأولية التي تؤخذ في اللحظة غالباً ما تحتوي على أسماء وتوجيه أصابع الاتهام - هذا أمر طبيعي، وليس عيباً في الشخصية. مهمة الـ Prompt هي تنقية ذلك إلى لغة قائمة على الأدوار والأنظمة قبل أن يصبح وثيقة دائمة تُقرأ من قبل القيادة ويُشار إليها لسنوات. هذا هو ما يجعل تقرير ما بعد الحادث آمناً للكتابة بصدق في المقام الأول: لا أحد يجب أن يقلق من أن اسمه سينتهي في مستند سيتم توزيعه.
لماذا يهم الحد الأقصى لبنود العمل
قائمة غير محدودة من بنود العمل هي قائمة لا ينفذها أحد. الحد الأقصى بخمسة يفرض تحديد الأولويات الحقيقية - يجب على النموذج أن يقرر أي الإصلاحات تعالج بالفعل السبب الجذري والعوامل المساهمة، بدلاً من حشو التقرير بكل فكرة تحسين ذات صلة هامشية ظهرت أثناء الحادث.
لماذا يتم الإشارة إلى عدم اليقين بدلاً من تجميله
تقرير ما بعد حادث يبدو واثقاً بشأن تفاصيل لم يؤكدها أحد فعلياً هو أسوأ من التقرير الصادق بشأن ثغراته. علامة [NEEDS CONFIRMATION] تمنع النموذج من اختلاق أرقام أو طوابع زمنية تبدو دقيقة لم تكن موجودة في الملاحظات الأصلية - وهو نمط فشل شائع عندما يُطلب من الذكاء الاصطناعي جعل مدخلات فوضوية تبدو مصقولة.
الصق ملاحظات الحوادث الأولية، واملأ حقول السياق الثلاثة، وسيكون الناتج مستنداً جاهزاً لمراجعة القيادة - مع جدول زمني، وتحليل حقيقي للسبب الجذري، وبنود عمل يمكن لشخص ما البدء في العمل عليها اليوم.