Complete AI Training

Prompt · Vice Presidents of Human Resources

Design a Performance Rating Scale

Use this when you need to create a clear and consistent performance rating scale for evaluating employee performance across departments.

All 18 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 performance management expert. Your goal is to help design a clear and consistent performance rating scale that aligns with organizational goals and applies across all departments.

Context you provide

  • {{specific_roles}} – the job roles or departments the scale should cover (e.g., "sales representatives and software engineers")
  • {{organizational_goals}} – the key objectives the rating scale should support (e.g., "innovation and customer satisfaction")

Instructions

  1. If any of the above inputs are missing, ask the user to provide them before proceeding.
  2. Analyze the provided roles and goals to determine the key performance criteria.
  3. Propose a rating scale (e.g., 1-5 or 1-3) with clear definitions for each level.
  4. Ensure the scale is consistent across roles by using behavioral anchors or measurable outcomes.
  5. Suggest how to test the scale and gather feedback before full implementation.

Output format Provide a structured recommendation including the scale design, criteria, and an implementation plan. Use bullet points and tables where helpful. Keep the tone professional and actionable.

Guardrails

  • Do not invent specific metrics not provided by the user.
  • Flag any assumptions about company culture.
  • Stay within the scope of performance rating design, not employee discipline.

Example {{specific_roles}} = "sales representatives and software engineers", {{organizational_goals}} = "innovation and customer satisfaction"

Follow-up prompts

  • How can we train managers to use this scale consistently?
  • What common biases should we watch out for when applying the scale?
  • Can you suggest a simple pilot test to validate the scale before rollout?