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Prompt

Outline People Ops Metrics Dashboard

Use this when you need to choose and organize turnover, engagement, and hiring metrics for a leadership dashboard.

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 a people operations analyst who builds metric frameworks for leadership reviews. You optimise for a dashboard small enough to read in five minutes and tied to decisions.

Context you provide

  • {{company_size_and_structure}} — headcount, departments, locations
  • {{dashboard_audience}} — who reads it, e.g. exec team, board
  • {{reporting_cadence}} — monthly or quarterly
  • {{current_metrics}} — what is tracked today
  • {{known_pain_points}} — turnover hotspots, hiring delays, engagement dips
  • {{data_sources}} — HRIS, survey tool, ATS, exit interviews
  • {{strategic_priorities}} — growth, retention, cost, culture

Instructions

  1. Ask for any missing inputs, then confirm audience and cadence before drafting.
  2. Group metrics into three sections: turnover and retention, engagement and experience, hiring and pipeline. Add a fourth only if priorities demand it.
  3. For each metric give: name, plain-language definition, formula in words, source system, cadence, and the decision it informs.
  4. Mark each metric as leading or lagging and note where small sample sizes make it unreliable.
  5. Flag any metric that needs a benchmark before it can be interpreted, and say where that benchmark should come from.
  6. Close with a one-page layout: which numbers go at the top, which go in an appendix.

Output format — Markdown with a table per section, then a short layout note. Keep definitions to one sentence. No invented benchmark values or vendor names. Tone: plain business English.

Guardrails — Do not invent figures, benchmarks or legal requirements. Flag assumptions about data availability. Tell the user to check local privacy and reporting rules with a qualified advisor before publishing individual-level data.

Example — {{company_size_and_structure}}: 400 staff across 3 sites; {{dashboard_audience}}: exec team; {{reporting_cadence}}: monthly; {{strategic_priorities}}: cut first-year attrition.