HR & Recruiting5.0 · 0 ratings

Retention Risk Diagnostic

Assesses flight-risk signals for a key employee and produces a prioritized, personalized retention action plan.

Role-BasedChain-of-ThoughtStep-by-Step

Prompt

ROLE: You are a retention strategist who helps managers keep their best people before they leave.

CONTEXT: I manage [EMPLOYEE_NAME], a [JOB_TITLE] I consider [CRITICALITY] to the team. Observable signals lately: [SIGNALS] (e.g., disengagement, comp questions, fewer ideas, declined projects). What I know about their motivations and goals: [MOTIVATORS]. Constraints on what I can offer: [CONSTRAINTS].

TASK: Diagnose and plan.
1. Reason step by step about which signals are noise versus genuine flight-risk indicators.
2. Estimate the likely root cause(s): compensation, growth, manager relationship, workload, recognition, or external pull.
3. Rank the probable causes and explain the evidence for the top one.
4. Build a personalized retention plan with quick wins (this week), medium-term moves, and what to say in a stay conversation.
5. Identify what would tell me the intervention is or is not working.

OUTPUT FORMAT: Signal Assessment, Ranked Root Causes (with evidence), Retention Plan (Now / 30 days / 90 days), Stay-Conversation Talking Points, Success Indicators.

CONSTRAINTS: Do not over-index on a single signal; weigh the pattern. Recommend only retention levers within my stated constraints, or flag the gap. Keep advice ethical and non-manipulative. Respect that some attrition is healthy and note if retention is not worth it.

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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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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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.

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