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AI Prompts for Interview Question Banks

An interview question bank is only as good as the mapping behind it: each question tests a named competency at a stated level, has probes that get to the candidate's own actions, and has a scoring rubric with anchored descriptions. Without that, a bank is a list of things to ask, and interviewers drift back to their favorites.

This page keeps the original interview-kit prompt and adds two around it: building a reusable bank organized by competency and level so kits can be assembled consistently across roles, and reviewing a set of questions for bias, legality and whether they actually predict anything.

Before you use these

Have these ready to replace the highlighted [variables]:

These prompts structure evidence against stated, job-related criteria and draft documents. They do not make hiring decisions. Do not ask the model to rank, reject or recommend candidates on protected characteristics or their proxies (name, address, school, gaps, age indicators), and do not treat its output as a decision: a person accountable for the hire reviews every score and makes the call. Check local law before using any automated or AI-assisted screening.

The prompts

1. Create a structured interview kit

Best forA ready-to-run kit for one role: behavioral questions mapped to competencies, scenario questions, probes, rubric and red flags.
Inputs needed
  • Job title
  • Competencies
How to use itThe original prompt from this page. List the competencies from intake so the kit maps to the success profile.
Expected outputSix behavioral questions mapped to competencies, three scenario questions, probes, a 1–5 anchored rubric, red flags, and guidance on questions to avoid.
Create a structured interview kit for hiring a [job title]. Include: 6 behavioral questions mapped to competencies [list competencies], 3 role-specific scenario questions, follow-up probes for each, a 1-5 scoring rubric with anchored descriptions, and red flags to listen for. Add guidance on questions to avoid for legal/bias reasons.

2. Build a reusable question bank by competency and level

Best forA bank that kits can be assembled from consistently, with level-appropriate versions of each question.
Inputs needed
  • Competency framework
  • Levels
  • Roles served
How to use itGive the framework. The model builds the bank with tagged questions, level variants, probes, anchors and usage notes — and a rule for how kits are assembled from it.
Expected outputBank structure, questions per competency × level with probes and anchors, usage notes, the kit-assembly rule, and the maintenance process.
Act as a talent assessment lead building an interview question bank for [company].

Competency framework: [competencies with behavioral indicators by level]
Levels: [e.g. associate, professional, senior, lead, manager]
Roles served: [families]

1. Bank structure: competency → level → questions, each tagged with the indicators it tests, the stage it suits, and the roles it applies to.
2. For each competency and level: two behavioral questions and one scenario or work-sample question; probes; anchored 1–4 scoring with example evidence per level (what 'senior' evidence looks like versus 'associate').
3. Usage notes per question: what it discriminates well, common weak answers that sound strong, contexts where it does not apply and the alternative.
4. Kit-assembly rule: how a role's kit is built from the bank (competencies from intake → level → one question per competency per stage), so every candidate for a role gets the same set.
5. Maintenance: how questions are retired (over-exposed, poor discrimination), how new ones are added, who owns the bank.
6. Governance: legal review of new questions; the list of topics never included.

Write the questions in full. Level variants must differ in the depth of judgment expected, not just in wording.

3. Review a question set for bias and predictive value

Best forAuditing questions already in use before they are reused.
Inputs needed
  • Question set
  • Competencies claimed
  • Any outcome data (interview scores vs performance)
How to use itPaste the questions. The model checks each for job-relatedness, bias exposure, legality flags, answer-ability across backgrounds, and whether it can discriminate between candidates.
Expected outputQuestion-by-question review with keep / revise / remove, rewrites, legal flags to verify, and the questions that add nothing.
Act as an assessment reviewer auditing an interview question set for [role / company].

Questions: [paste with the competency each claims to test]
Outcome data: [if any: scores vs later performance; interviewer feedback]

For each question:
1. Job-relatedness: does it test a defined competency with behavioral evidence, or an impression (enthusiasm, 'fit', hobbies)?
2. Bias exposure: does it favor candidates from particular backgrounds, companies or life circumstances without job reason? Rewrite to an equivalent that any qualified candidate could answer.
3. Legality flags: anything touching protected characteristics or their proxies (family, health, age, origin, religion, union) — mark for removal and verification with HR/legal.
4. Predictive value: hypotheticals that reward articulate guessing; questions with 'known' right answers; questions every candidate rehearses. Replace with past-behavior versions.
5. Discrimination between candidates: questions everyone answers the same way add nothing; remove or sharpen.
6. Verdict: keep / revise (with the rewrite) / remove, and the competency left uncovered if removed.

Then: the three highest-risk questions currently in use and the immediate replacement for each.

Related prompts

Logical next step

After this, most HR teams move on to Structured Interview Design.

Get the free HR & Recruiting AI Starter Kit → Nine of these prompts as an intake → assess → offer hiring workflow with an intake worksheet, delivered by email. See what's inside

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