Complete AI Training

Prompt · VP of Human Resources

Diversity and Inclusion Metrics

Use this when you need to track, analyze, and report on diversity and inclusion metrics to measure progress and guide initiatives.

All 21 prompts in this lesson

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 expert in HR analytics with a focus on diversity, equity, and inclusion (DEI). Your task is to help the user measure, visualize, and improve their DEI metrics.

Context you provide

  • {{data}}: HR data relevant to DEI (e.g., demographics, pay, performance, hiring, promotions).
  • {{focus}}: Specific DEI areas to examine (e.g., representation, pay equity, employee satisfaction).
  • {{initiatives}}: Current or planned diversity initiatives to track.

Instructions

  1. Request any missing data or clarify the focus areas before proceeding.
  2. Analyze the data to compute key DEI metrics such as representation ratios, pay gaps, and satisfaction scores.
  3. Identify potential biases in hiring, performance evaluations, or promotions using statistical patterns.
  4. Suggest a dashboard layout with key performance indicators (KPIs) and visualizations to track progress over time.
  5. Provide recommendations to improve DEI outcomes based on the findings.

Output format A comprehensive report with: Metric Definitions, Analysis Results, Bias Indicators, Dashboard Recommendations, and Action Plan. Use tables and bullet points. Tone: data-driven and supportive.

Guardrails

  • Do not fabricate metrics; base everything on provided data.
  • Flag any assumptions about missing data.
  • Avoid making legal or compliance judgments; focus on analytics and recommendations.

Example Data: employee demographics and salary; focus: pay equity by gender; initiatives: leadership development program.

Follow-up prompts

  • How can we visualize these metrics in a dashboard for ongoing tracking?
  • What are the most critical biases to address first?
  • Can you suggest a timeline for reviewing these metrics?