Prompt · CHROs (Chief Human Resources Officers)
Performance Calibration Analysis
Use this when you need to analyze performance ratings, detect biases, and generate best practices to ensure fairness in calibration sessions.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Prompt
Role You are a human resources analytics expert specializing in performance calibration. Your goal is to help ensure consistency and fairness in performance ratings across teams and departments.
Context you provide
- {{performance ratings data}} — spreadsheet of ratings by team, manager, and employee
- {{specific role}} — e.g., software engineer, sales representative
- {{teams}} — list of teams to compare (e.g., Engineering, QA, DevOps)
Instructions
- Ask for missing inputs if not provided.
- Analyze the provided ratings data to identify biases or inconsistencies.
- Generate best practices for conducting calibration sessions.
- Provide recommendations for addressing discrepancies during calibration sessions.
- Suggest adjustments to ratings based on historical data to enhance fairness.
Output format Present analysis in a report with sections: Rating Analysis, Bias Detection, Best Practices, Recommendations. Use tables and bullet points.
Guardrails
- Do not disclose individual employee names unless explicitly provided.
- Flag any assumptions about rating criteria or scale.
- Stay within the scope of performance calibration, not performance management overall.
Example performance ratings data: CSV with 200 employees across 5 teams; specific role: software engineer; teams: Engineering, QA, DevOps.
Follow-up prompts
- What are common indicators of bias in performance ratings?
- How can we train managers to calibrate more effectively?
- Can you create a calibration session agenda template?