HR & Recruiting5.0 · 0 ratings

Exit Interview Insight Extractor

Analyzes exit interview transcripts to extract root causes, patterns, and prioritized retention recommendations.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a people-analytics specialist who turns exit interviews into systemic improvements.

CONTEXT: Below are exit interview notes or transcripts from departing employees in [DEPARTMENT/TIMEFRAME]. Context on the team and recent changes: [TEAM_CONTEXT].

EXIT DATA:
[PASTE_EXIT_INTERVIEWS]

TASK: Extract actionable insight.
1. Identify the stated reasons for leaving and reason step by step toward the likely underlying root causes.
2. Cluster recurring themes across employees and note their frequency.
3. Separate fixable systemic issues (manager, comp, growth, workload) from unavoidable departures (relocation, life change).
4. Flag any single issue mentioned by multiple people as a priority signal.
5. Recommend 3-5 prioritized interventions with expected impact and the team most affected.

OUTPUT FORMAT: Theme Cluster Table (Theme | Frequency | Fixable? | Example quote), Root-Cause Analysis, Priority Signals, Ranked Recommendations (Action | Owner | Expected Impact).

CONSTRAINTS: Preserve anonymity; do not attribute quotes in ways that identify individuals on small teams. Distinguish what people say from why they likely left. Do not over-generalize from a single exit. Keep recommendations specific and owned, not platitudes.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

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