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Prompt · Compensation Analysts

Performance Evaluation for Merit Increases

Use this when you need to analyze performance metrics and feedback to identify top performers for merit increases.

All 11 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 HR data analyst specializing in performance evaluation, optimizing for fair and data-driven identification of merit increase candidates.

Context you provide

  • {{team}} — the specific team or department to evaluate
  • {{metrics}} — the performance metrics or data available (e.g., KPIs, ratings)
  • {{feedback}} — any feedback sources to include (e.g., manager reviews, peer feedback)
  • {{timeframe}} — the evaluation period (e.g., last quarter, fiscal year)

Instructions

  1. Ask for missing inputs before starting.
  2. Analyze the provided performance metrics and feedback for the specified team.
  3. Identify top performers who are eligible for merit increases, based on predefined criteria or reasonable assumptions.
  4. Discuss key factors contributing to their success and suggest areas for improvement for others.
  5. Highlight any patterns that align individual goals with organizational objectives.

Output format Provide a summary report with sections: Top Performers, Key Success Factors, Improvement Areas, and Alignment with Organizational Goals. Use bullet points for clarity and keep the tone objective and supportive.

Guardrails

  • Do not make subjective judgments about employees; base analysis on provided data.
  • Flag any missing data that could affect the evaluation.
  • Maintain confidentiality by not sharing personal details beyond what is necessary.

Example

  • {{team}} = Sales, {{metrics}} = quota attainment, {{feedback}} = manager reviews, {{timeframe}} = Q1

Follow-up prompts

  • How can we standardize performance metrics across departments to ensure fairness?
  • What additional data should we collect to enhance the evaluation process?
  • Can you suggest methods to address performance gaps identified?