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AI prompts for HR and recruiting: job descriptions, interview questions, and evaluations

Practical AI prompt templates for HR teams and recruiters — writing job descriptions that attract the right candidates, preparing structured interview question sets, and summarizing candidate evaluations.

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Where AI saves HR and recruiting teams real time
  • Job description first drafts: a well-prompted AI can produce a first draft in under a minute. You still edit for accuracy and culture fit, but starting from a structured draft is faster than a blank document — especially for roles you hire infrequently.
  • Interview question banks: given a job description and the competencies you are evaluating, AI can generate a structured question set in seconds. Review for relevance and legal compliance, but the generation step is instant.
  • Candidate evaluation summaries: after an interview, pasting your notes and asking AI to structure them into an evaluation summary saves time and improves consistency across interviewers.
  • Offer letter and rejection email drafts: routine correspondence follows a pattern. AI can draft these faster than a template library for common variations.
  • Job description language audit: paste an existing job description and ask AI to flag language that may discourage qualified candidates (unnecessarily gendered terms, excessive requirements, vague qualifications). This catches patterns that are hard to see from inside.
What AI cannot do in hiring — and what to verify
  • AI cannot evaluate candidates: a summary of interview notes is not an evaluation. Hiring decisions involve judgment about culture, potential, and team dynamics that AI does not have access to.
  • Bias amplification risk: if you prompt AI with biased inputs (a job description that skews toward a particular demographic, or a rubric that rewards background over demonstrated skill), it will produce biased outputs. Review AI-generated content for this before using it.
  • Legal compliance: employment law varies by jurisdiction. AI-generated job descriptions, rejection letters, and evaluation criteria need legal review before they become standard practice — AI is not a lawyer and will not flag jurisdiction-specific compliance issues reliably.
  • Sensitive conversations: compensation negotiations, performance improvement plans, termination conversations, and accommodation requests require human judgment and cannot be scripted by AI.
  • Reference checks: AI can help you write reference check questions. It cannot conduct the call, interpret tone, or judge credibility — that is the recruiter's job.
Templates
Job description first draft
Write a job description for the role described below.

Role details:
- Job title: [e.g. Senior Product Designer]
- Team / reporting line: [e.g. Design team, reports to Head of Design]
- Location and work arrangement: [e.g. Remote (US only), hybrid (New York), on-site]
- Employment type: [Full-time / Part-time / Contract]
- Seniority: [e.g. Senior IC, no direct reports]
- Core mission of the role (1–2 sentences): [what this person will own]
- 3–5 core responsibilities: [list them — be specific, not generic]
- Must-have qualifications: [skills, experience, or credentials that are truly required]
- Nice-to-have qualifications: [genuinely optional — do not list these as requirements]
- What success looks like in 90 days: [one concrete outcome]
- Anything we must NOT include: [e.g. salary range (listed separately) / specific tool names that would over-constrain / anything legally sensitive]

Rules:
- Lead with the role's impact, not the company description
- Use "you will" not "the candidate will" — address the reader directly
- Keep qualifications honest: if a nice-to-have is listed as required, it discourages qualified candidates
- Under 400 words total
- No jargon, no superlatives ("world-class", "rockstar", "ninja")
- End with a brief, factual company description (1–2 sentences max)
Opens on home with the prompt prefilled.
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Structured interview question set
Generate a structured interview question set for the role and competencies listed below.

Role: [Job title]
Interview type: [e.g. Behavioral / Technical / Case / Culture / Final round]
Interview length: [e.g. 45 minutes]
Interviewer's focus: [e.g. problem-solving and collaboration — the hiring manager is covering technical skills in a separate round]

Core competencies to evaluate in this interview (pick 3–4 max):
1. [Competency 1, e.g. "Prioritization under constraints"]
2. [Competency 2, e.g. "Cross-functional communication"]
3. [Competency 3, e.g. "Dealing with ambiguity"]

For each competency, provide:
- 1 primary behavioral question (past-tense STAR format: "Tell me about a time...")
- 1 follow-up probe to use if the answer is vague ("Can you tell me more about how you decided..." / "What would you do differently now?")
- 1 signal to listen for in a strong answer (what a good response includes — not a "right answer")

Also include:
- 1 opening question to set the candidate at ease (not a competency question)
- 1 closing question to give the candidate space to ask or add something

Format each competency as:
Competency: [name]
Primary: [question]
Follow-up: [probe]
Strong signal: [what to listen for]

Rules:
- Questions must be legal in [jurisdiction, e.g. "the US" / "Argentina"] — no questions about age, family, origin, or protected characteristics
- Use behavioral framing ("tell me about a time") not hypothetical ("what would you do if")
- Keep each question under 30 words
Opens on home with the prompt prefilled.
Open in Promptea
FAQ
Can AI help reduce bias in job descriptions?
Yes, in a targeted way. AI is good at flagging patterns in language that research has associated with lower application rates from certain groups — unnecessarily gendered words, long lists of 'requirements' that are really preferences, or vague qualifications that substitute for specific skills. Paste an existing job description and ask 'What language in this description might discourage qualified candidates? What specific changes would you suggest?' Review the suggestions critically — AI may over-flag in some domains — but it catches patterns that are genuinely hard to see from inside.
Is it ethical to use AI to screen resumes?
Automated resume screening carries documented risk of encoding historical hiring bias into the filter — systems trained on past hiring decisions tend to replicate past patterns, including ones you would not endorse if made explicit. Use AI to draft screening criteria (the rubric itself) and to draft questions for the human reviewer — not to rank or reject candidates autonomously. Keep a human in the loop for every screening decision, and audit the criteria for bias before applying them at scale.