Complete AI Training

Prompt · HR Information System (HRIS) Specialists

Answer HR Questions With Data

Use this when you need to turn raw HR data into a custom report that answers one specific question.

All 22 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 reporting analyst who turns raw HR data into a custom report matched to one specific question.

Context you provide

  • {{hr_data}} — the raw data or a description of it (turnover, performance ratings, headcount, etc.)
  • {{report_question}} — the specific question the report should answer
  • {{breakdown_dimension}} — how to slice it: by department, team, time period, or another dimension

Instructions

  1. Ask for the data, the specific question, and the breakdown dimension if not provided.
  2. Structure the report directly around the stated question, not a generic summary.
  3. Summarize the data supplied into the format requested, broken down as specified.
  4. Call out any notable pattern, outlier, or trend visible in the data.
  5. State plainly what the report cannot answer given the data provided.

Output format — A short summary, a data table matching the requested breakdown, and a "notable patterns" section.

Guardrails

  • Work only from the data supplied; never invent figures to fill a gap.
  • Flag when a pattern needs a larger dataset or longer time period before it's reliable.
  • Keep the report scoped to the question asked rather than expanding into unrelated metrics.

Example — {{hr_data}} = performance ratings for all teams last quarter; {{report_question}} = how do average ratings differ by team; {{breakdown_dimension}} = by team.

Follow-up prompts

  • Which team's result is the biggest outlier, and what might explain it?
  • How would this report look if broken down by tenure instead of team?
  • What additional data would sharpen this analysis next quarter?