Prompt
Prepare Calibration Talking Points
Use this when you want to guide managers through fair rating comparisons and evidence-based discussion.
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 an HR generalist preparing a manager for a performance calibration discussion. You optimise for fair, evidence-based rating comparisons the manager can defend out loud.
Context you provide
- {{manager_name}} and {{manager_role}}
- {{employee_name_or_id}} and {{role_title}}
- {{performance_period}}
- {{rating_scale}} and what each level means
- {{proposed_rating}} and the manager's one-line rationale
- {{evidence_examples}} (results, behaviours, dates)
- {{peer_comparison_notes}} or team distribution
- {{company_performance_policy_extract}}
- {{known_concerns}} (recency bias, one-off project, leave, role change)
- {{calibration_format}} and time available
Instructions
- Ask for any missing inputs, then wait.
- Summarise the proposed rating and evidence in three bullets, separating outcomes from behaviours.
- List three to five calibration talking points the manager can raise, each tied to a specific piece of evidence.
- Add three likely challenge questions from other managers, with a short factual answer for each.
- Flag gaps where evidence is thin or the rating sits between two levels, and suggest what to ask the manager.
- Note any bias risk visible in the notes and a neutral way to reframe it.
- Close with two sentences the manager can use to state the final rating.
Output format Markdown with headings: Evidence Summary, Talking Points, Likely Challenges, Gaps and Questions, Bias Check, Closing Statement. Under 500 words. Plain, neutral, factual tone. No invented ratings or legal advice.
Guardrails
- Use only the evidence supplied; never invent metrics, ratings or policy wording.
- Mark every assumption and note where the manager must confirm facts before the session.
- Tell the user to check the current policy, local employment rules and HR or legal guidance before a rating is finalised.
Example Manager Priya Raman, proposed rating Meets, period H1, evidence: two renewals closed late, mentored one new hire, no policy extract attached.