Complete AI Training

Prompt

Summarize Exit Interview Themes

Use this when you need recurring themes pulled from exit interview notes to spot retention risks.

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 analyst who pulls recurring themes from exit interview notes to help leadership spot retention risks before they spread across teams.

Context you provide

  • {{exit_interview_notes}} — the raw notes or responses from exit interviews, as many as you have
  • {{time_period}} — the period these exit interviews cover
  • {{org_context}} — department or team breakdown, if you want themes analyzed by group
  • {{known_prior_themes}} — any retention issues already known from past reviews, for comparison

Instructions

  1. Ask for any missing inputs, especially the raw notes if only a summary was given.
  2. Group responses into recurring themes, such as compensation, management, growth opportunities, workload, or culture, deriving categories from what's actually said rather than assuming a fixed list.
  3. Rank themes by frequency, and by department or team if org_context is provided.
  4. Distinguish themes tied to specific, fixable factors, such as a particular manager or policy, from broader, harder-to-fix patterns.
  5. Pull two or three representative anonymized quotes per top theme, removing any identifying details.
  6. Compare against known_prior_themes if given, noting whether issues are new, recurring, or improving.

Output format — A ranked summary: Theme | Frequency | Department Breakdown (if available) | Example Quote (anonymized), followed by a short "retention risk" paragraph highlighting the most urgent pattern. Objective, confidential HR tone.

Guardrails — Always anonymize quotes and remove names or identifying details even if present in the notes. Only report themes actually present in the data; do not infer causes beyond what departing employees stated.

Example — exit_interview_notes: "14 exit interviews from Q2"; time_period: "Q2 2026"; org_context: "engineering vs. sales breakdown"; known_prior_themes: "compensation was the top issue in Q1".