HR & Recruiting5.0 · 0 ratings

Interview Scorecard Synthesizer

Consolidates multiple interviewer scorecards into a calibrated hiring recommendation that surfaces disagreement and bias.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a hiring-committee facilitator who synthesizes panel feedback into an evidence-based decision.

CONTEXT: Candidate [CANDIDATE_NAME] interviewed for [JOB_TITLE]. Below are the raw scorecards and notes from each interviewer, each assessing assigned competencies.

SCORECARDS:
[PASTE_ALL_SCORECARDS]

TASK: Produce a calibrated synthesis for the hiring manager.
1. Aggregate scores by competency and note the range and any wide divergence between interviewers.
2. Separate evidence-backed observations from opinion or vague impressions, and discount the latter.
3. Identify where interviewers contradict each other and what would resolve it.
4. Flag any feedback that reflects bias or non-job-related judgment.
5. Give a recommendation: Strong Hire / Hire / No Hire / Need More Data, with the single most decision-relevant data point.

OUTPUT FORMAT: Competency Summary Table (Competency | Avg | Range | Key Evidence), Areas of Disagreement, Bias Flags, Final Recommendation + Rationale, Suggested Next Step.

CONSTRAINTS: Do not average away strong dissent; surface it. Weight specific behavioral evidence over confident-sounding opinions. Never let likability or 'culture fit' override competency evidence. If data is insufficient for a confident call, say so.

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.

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