Step01
Create a Detailed Rubric
Write the role rubric with competencies, observable evidence, and what strong, adequate, and weak documentation looks like. Keep candidate evaluation criteria separate from scorecard writing quality.
BeginnerOperations
How to Improve Interview Feedback Consistency with a Custom GPT
Use a custom GPT to coach interviewers on the quality of their scorecards. A role rubric plus strong and weak examples produces a consistent rating, specific feedback, and a short Slack message the hiring manager can send.
Source episode · 36:40
How I AI: Zach Davis's 3 Workflows for Enterprise Engineering with AIwith Claire Vo
Zach pastes an interview scorecard into a custom GPT trained on panel rubrics and examples, then receives a quality rating, coaching points, and a concise Slack note.
Before you start
What you need
- A role specific interview rubric
- Examples of strong and weak scorecards
- A custom GPT or private equivalent
- Scorecards with candidate data handled under company policy
What you’ll make
A consistent scorecard quality review with evidence based coaching and a concise message for the interviewer.
Tools used
- ChatGPTVisitChatGPT
OpenAI conversational AI
Step by step
The workflow
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
4 steps
Step02
Develop a GPT Prompt
Build the custom GPT with the rubric, strong and weak scorecard examples, and matching coaching examples. Define a fixed output: quality rating, evidence, strengths, improvements, and Slack note.
Example prompt
Evaluate the quality of interview scorecards, not the candidate. Use the attached role rubric and examples. Rate each scorecard Excellent, Good, Fair, or Poor. Cite specific scorecard text, identify strengths, missing evidence, rubric misuse, and any subjective or biased language. Then draft a short, respectful Slack note with the two most important improvements. Do not make a hire or no hire recommendation.
Step03
Process Scorecards with the GPT
Paste the scorecard after removing data that should not enter the model. Ask the GPT to evaluate the writing against the rubric, with direct references to claims that need more evidence.
Example prompt
Review this scorecard for documentation quality using the project rubric. Evaluate whether claims are specific, job related, supported by interview evidence, and mapped to the right competency. Return the fixed review format and Slack coaching note. [scorecard]
Step04
Generate Standardized Feedback
Read the detailed feedback and edit the Slack note before sending it. Use repeated findings to improve interviewer training and rubric examples so scorecard quality rises over time.
What good looks like
- The GPT evaluates the scorecard quality, not the candidate.
- Feedback references the rubric and specific scorecard text.
- Missing evidence and vague claims are called out.
- The Slack note is clear, respectful, and actionable.
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After the steps
Runbook notes
How to recover when the loop fails and where human judgment helps.
Recover
If it goes sideways
- The GPT makes a hiring recommendation instead of coaching the scorecard
- State that the task is to evaluate documentation quality only and exclude candidate selection from the output schema.
- Feedback reinforces subjective or protected characteristic judgments
- Require job related evidence, remove protected data, and flag unsupported personality or culture claims for rewrite.
- Every scorecard receives the same advice
- Include contrasting examples and require quotations or field references from the submitted scorecard.
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