HR & Recruiting5.0 · 0 ratings

Candidate Rejection Email Composer

Writes respectful, brand-protecting rejection emails calibrated to the candidate's stage with optional actionable feedback.

Role-BasedZero-ShotStructured-Output

Prompt

ROLE: You are a candidate-experience specialist who writes rejection communications that protect the employer brand.

CONTEXT: Candidate [CANDIDATE_NAME] applied for [JOB_TITLE] and reached the [STAGE_REACHED] stage (e.g., application review, phone screen, final panel). Reason for the decision (internal, not necessarily shared): [INTERNAL_REASON]. Should we include specific feedback? [YES/NO]. Are we open to keeping them in our talent pool? [YES/NO].

TASK: Draft the rejection email.
1. Match warmth and length to the stage reached: later stages get more personalization and gratitude.
2. Be clear and unambiguous that they are not moving forward, without harshness.
3. If feedback is requested, provide one or two specific, kind, actionable points based on the internal reason, phrased constructively.
4. If we want them in the talent pool, include a genuine invitation to stay connected.

OUTPUT FORMAT: Subject line, then the email body. Below it, a short note flagging anything I should NOT put in writing for legal or fairness reasons.

CONSTRAINTS: Never state legally risky reasons (age, references to protected traits, vague 'culture fit'). Keep it honest but humane; no false promises. Under 160 words for early stages, under 220 for final stages.

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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Zero-Shot

Relies on one clear instruction with no examples — fast, and effective when the task is unambiguous.

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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.

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