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Section IV · For the Task

Prompts for interviewing candidates

Prompts for structured interview questions and candidate scorecards.

§ Overview

Good hiring is structured hiring, and structure is exactly what AI can scaffold before you ever meet a candidate. The Generate Interview Questions for a Role prompt builds a balanced set across technical, behavioral, and culture-fit categories, so you walk in with a plan instead of improvising the same three questions you always ask. The Build a Structured Hiring Scorecard prompt then gives you competencies with rating anchors and clear disqualifiers, which is how you compare candidates fairly instead of on gut feel.

The collection covers the full arc around the interview. The Write a Compelling Job Description prompt shapes an inclusive, outcome-focused posting that attracts the right people in the first place, and the Build a 30-60-90 Day Onboarding Plan prompt prepares for the hire you're about to make so the offer leads somewhere real. The pitfall AI helps you avoid is the unstructured interview, where vague questions and inconsistent evaluation let bias and charisma do the deciding. Asking for behavioral questions tied to real competencies, with anchored scoring, is the antidote, and it's also what holds up if a hiring decision is ever questioned.

§ Field Notes

What makes a good prompt for interviewing candidates

A strong interview prompt gives the model the role's actual responsibilities and the few competencies that genuinely predict success, then asks for questions mapped to each one. Generic "tell me about yourself" sets evaluate nothing; behavioral questions tied to a specific competency ("describe a time you shipped under a hard deadline") give you comparable signal across candidates.

The scorecard is what makes it fair. Ask for explicit rating anchors, what a 2 versus a 4 actually looks like for each competency, and clear disqualifiers, so two interviewers grade the same answer similarly. For the job description, tell the model to write for outcomes and inclusivity and to cut the inflated requirements that scare off strong candidates. Structure up front is what turns interviewing from a vibe check into a decision you can defend.

§ Pro Tips

Get sharper results

  • 01Give the model the role's real responsibilities and the three to five competencies that actually predict success, so questions test what matters instead of filling time.
  • 02Ask for behavioral, past-experience questions tied to each competency, since they produce comparable signal where hypotheticals just reward confident talkers.
  • 03Request explicit rating anchors on the scorecard (what a 2 versus a 4 looks like) so multiple interviewers grade consistently and bias has less room.
  • 04Have it draft follow-up probes for each question so you can dig past a rehearsed answer to the real story underneath.
§ FAQ

Common questions

Won't AI-generated interview questions be too generic to be useful?

They are if you only give it a job title. Feed it the specific responsibilities and the competencies you're hiring for, and ask for behavioral questions tied to each. The more concrete your input about what the role actually requires, the more the questions test real fit rather than surface polish.

How does a structured scorecard reduce bias?

By forcing every candidate through the same questions and the same anchored rating scale, it replaces 'I just liked them' with evidence you can compare. Ask the model for clear definitions of each score so interviewers aren't quietly grading on charisma. It won't eliminate bias, but it makes inconsistent evaluation much harder.

Can I use AI to evaluate a candidate's actual answers?

Be careful here. It's fine to use AI to design questions and scorecards, but running a real person's responses or application through a model to score them raises fairness, privacy, and legal concerns. Keep the human judgment on candidates with the humans, and use AI for the preparation.

§ The Prompts · 6