Interview Scorecard Synthesizer
Consolidates multiple interviewer scorecards into a calibrated hiring recommendation that surfaces disagreement and bias.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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.
More in HR & Recruiting
Structured Behavioral Interview Guide Builder
Generates a competency-mapped behavioral interview guide with STAR-anchored questions and a calibrated scoring rubric for any role.
Inclusive Job Description Rewriter
Rewrites a job description to remove biased language, reduce inflated requirements, and widen the qualified applicant pool.
Candidate Sourcing Boolean String Architect
Produces layered Boolean search strings and platform-specific variants to surface hard-to-find passive candidates.
Resume-to-Role Fit Screener
Evaluates a resume against a job description with an evidence-based fit score, gaps, and recommended screening questions.