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Generate Interview Questions for a Role

Build a structured interview question set with technical, behavioral, and culture-fit categories.

Optimized for
ChatGPTClaude
§ When to use this

This prompt builds a structured interview kit split into three buckets — skills, behavioral (STAR), and culture-fit — instead of a random list of questions. Reach for it the day before an interview when you want coverage of the role's actual must-haves rather than generic 'tell me about yourself' filler. Because each question comes with a note on what a strong answer looks like, it doubles as a scoring rubric for less-experienced interviewers on the panel.

§ The Prompt— fill in the fields, then copy or open in a tool
§ Customize0/3 fields filled
your prompt — fill the fields above
You are a hiring manager preparing to interview a candidate for [ROLE] at a [COMPANY STAGE/TYPE]. Generate: (1) 5 technical/skills questions matched to the role's must-haves, (2) 3 behavioral questions using the STAR framework, (3) 2 culture-fit questions specific to a [COMPANY CULTURE DESCRIPTION] environment. For each question, briefly note what a strong answer looks like.
Open with your prompt →ChatGPTClaudeSends your filled-in prompt straight into a new chat.
§ Example Output

What you can expect back

Technical / Skills
1. Walk me through how you'd design an idempotent payment-processing endpoint in Go. Strong answer: discusses idempotency keys, DB constraints, retries, and failure modes — not just happy path.
2. How do you debug a goroutine leak in production? Strong answer: pprof, runtime metrics, and a concrete past example.

Behavioral (STAR)
3. Tell me about a time you shipped something under a hard deadline with incomplete requirements. Strong answer: clear Situation/Task, owned the trade-off decision, names a measurable Result.

Culture Fit
4. Describe a time you disagreed with a peer's technical direction and how you handled it. Strong answer: shows direct but respectful conflict, comfort without heavy process, and a focus on outcome over ego.

Illustrative example — your results will vary by tool and inputs.

§ Pro Tips

Get sharper results

  • 01Paste the actual job description under the prompt — the model matches 'must-haves' far better from real bullet points than from the title alone.
  • 02Ask it to flag which technical questions are 'screening' versus 'differentiating' so a phone screen and an onsite use different subsets.
  • 03Request a follow-up probe for each question ('if the candidate gives a shallow answer, ask...') to keep weaker interviewers from accepting surface-level responses.
  • 04For senior roles, tell it to bias behavioral questions toward influence and leadership rather than individual execution.
  • 05Avoid culture-fit questions that just test sameness — ask the model to frame them around values and working style, not background, to reduce bias.
§ Variations

Adapt it for your case

Panel division

Ask it to assign each question to a specific interviewer role (hiring manager, peer, skip-level) so the loop doesn't repeat questions.

Take-home alternative

Request a small practical exercise plus a rubric instead of live technical questions.

Scorecard format

Have it output the questions as a 1-5 scoring table with anchor descriptions for each score level.

Use For — Tasks
Tags#hiring#interview#recruiting
§ FAQ

Common questions

How do I keep the questions legal and bias-free?

Frame culture-fit around working style and values, not personal background, and avoid anything touching age, family, or origin. You can explicitly ask the model to keep questions job-related and EEOC-safe.

Can it tailor to a specific candidate's resume?

Yes — paste the resume and ask it to generate questions that probe gaps or verify claimed accomplishments, which makes the behavioral round much sharper.

The technical questions feel too generic. How do I fix that?

Generic output usually means a generic input; add your real tech stack, the hardest problem the role will face, and concrete must-haves from the JD.

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