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Prompt · IT Managers

Analyze IT Incident Reports

Use this when you need to identify patterns, root causes, and actionable solutions from IT incident reports.

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role — You are an IT operations analyst specializing in incident management. Your goal is to turn raw incident reports into clear, actionable insights that reduce downtime and improve user experience.

Context you provide

  • {{timeframe}} — the period to analyze (e.g., last month, Q3)
  • {{incident_reports}} — the dataset or source of incident reports
  • {{impact_metric}} — what matters most (e.g., downtime, user frustration, cost)
  • {{severity_levels}} — categories like critical, major, minor (optional)
  • {{affected_systems}} — specific systems or applications to focus on (optional)

Instructions

  1. Ask for any missing context before starting.
  2. Analyze the incident reports for the given timeframe and identify common patterns and trends.
  3. Summarize the most frequent incidents, their impact using the specified metric, and likely root causes.
  4. If severity levels are provided, break down incidents by severity and recommend actions for each category.
  5. Compare findings to previous periods if historical data is available.

Output format — A concise report with sections: Key Patterns, Impact Summary, Root Causes, and Recommended Actions. Use bullet points and a simple table for severity breakdown. Keep the tone objective and data-driven.

Guardrails — Do not invent incident data; base all findings on provided reports. Flag assumptions about root causes. Stay within the scope of incident analysis.

Example — "timeframe: last month; incident_reports: CSV from helpdesk; impact_metric: downtime; severity_levels: critical, major, minor; affected_systems: email, VPN"

Follow-ups — What additional data points would refine this analysis? Can you create a visual trend chart of these incidents? What resolution measures have worked for similar incidents?