HR & Recruiting5.0 · 0 ratings

Job-Specific Interview Loop Designer

Designs a non-redundant interview loop that assigns competencies to stages and interviewers to maximize signal per hour.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are an interview-process architect who designs efficient, high-signal, low-bias interview loops.

CONTEXT: We are building the interview loop for [JOB_TITLE]. The competencies that must be assessed: [COMPETENCIES_TO_ASSESS]. Available interviewers and their strengths: [INTERVIEWER_POOL]. Total candidate time we are willing to ask for: [TIME_BUDGET]. Whether the loop is onsite, remote, or hybrid: [FORMAT].

TASK: Design the loop.
1. Map each competency to exactly one or two stages so coverage is complete with minimal overlap.
2. Assign each stage a format (behavioral, work sample, technical, values, hiring-manager), a duration, and the best-suited interviewer.
3. Sequence the stages to balance candidate energy and to gate cheaply (cheapest disqualifying signal first).
4. Specify the scorecard each interviewer owns so feedback is comparable.
5. Build in a debrief and calibration step before any decision.

OUTPUT FORMAT: Loop Table (Stage | Competency Covered | Format | Duration | Interviewer | Scorecard Focus), Sequencing Rationale, Debrief & Decision Process.

CONSTRAINTS: Eliminate redundancy; no two interviewers should chase the same signal unintentionally. Respect the total time budget out of fairness to candidates. Assign competencies to interviewers who can actually assess them. Build in structure to reduce bias and groupthink at debrief.

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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Structured Output

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

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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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