Recruiting & Interview Assistant
Write JDs, screen candidates, build interview outlines, and handle candidate communication
Write JDs, screen candidates, build interview outlines, and handle candidate communication
A reusable work template with realistic input, a defined output format and a human review point.
Run one real task, verify the critical facts and record the before-and-after time.
The most time-consuming parts of recruiting: writing JDs, screening resumes, creating interview questions, recording feedback, sending response emails. AI can give you frameworks and drafts — you trim and make final calls.
1) Job Descriptions
- Prompt:
You are a hiring manager. Write a JD based on the following:
Role: xxx, Level: xx
Team background: ...
Required skills / nice-to-haves / work model / location
Output: role highlights, key responsibilities, hard skills, soft skills, performance metrics, salary range (optional), application email. No exaggerated or misleading descriptions.
- Ask AI for both a "short version (social media)" and "detailed version (company website)" to keep messaging consistent.
2) Resume Screening & Scoring
- Have AI output "match score + evidence citations + areas of concern + clarification questions."
- For missing key info, ask AI to generate "questions to ask" rather than guessing.
- Quick note: include the instruction
Do not make biased judgments based on gender/age/schoolto reduce risk.
3) Interview Guides & Question Banks
- Generate "structured interview questions + scoring rubric + follow-up paths."
- For technical/case questions, have AI provide "expected answer points" and "common mistakes."
- For behavioral interviews: require STAR format examples and a scoring rubric.
4) Interview Notes & Feedback
- Feed in interview notes/recording summaries and ask AI to output:
Summary, strengths/risks, hire recommendation (with reasoning), info gaps, feedback email draft for the candidate. - For multiple interviewers' feedback, have AI merge and flag "conflicting points / items to confirm."
5) Offer/Rejection & Candidate Communication
- Have AI provide "respectful and concise" rejection templates in multiple languages.
- Offer attachments and sensitive terms must be confirmed by humans. Ask AI to flag "requires HR review" fields.
6) Risk & Compliance
- Anti-discrimination: instruct AI not to reference gender/age/marital/parental status.
- Privacy: if resumes or recordings contain sensitive info, use enterprise/private models or create redacted summaries.
- Documentation: interview conclusions need supporting evidence — avoid "gut feeling" assessments.
7) Practice
Pick a role you're currently hiring for. Have AI output both JD versions, an interview guide, a scoring rubric, and rejection/feedback templates. Use a real resume (redacted) to generate "match score + clarification questions," then adjust manually.
8) Worked Example: From JD to a Structured Interview Decision
Suppose the role requires someone to handle a production incident independently. Translate that requirement into observable behaviour rather than “works well under pressure”:
| Capability | Observable evidence | Insufficient signal |
|---|---|---|
| Diagnosis | Narrows the scope in sequence and explains what to check first | Merely names monitoring tools |
| Risk judgement | Knows when to roll back, escalate, or stop | Optimises only for speed |
| Communication | Updates impact and next steps for different audiences | Says “I communicate well” |
Generate questions from the same rubric:
Capability: production incident handling
Create a structured interview pack:
1. Primary question with a concrete but incomplete incident scenario
2. Two levels of evidence-seeking follow-up
3. Behaviour anchors for scores 1, 3, and 5
4. A note field that records only what the candidate actually says
Do not use age, school, gender, nationality, or family status as signals.
Merge Evidence After the Interview
Below are de-identified notes from three interviewers.
Organise by capability:
- direct evidence supporting hire
- risk evidence
- contradictions between interviewers
- information that remains untested
Do not make the final hiring decision or invent missing statements.
9) Fairness and Privacy Gate
- Remove name, phone, address, and photo before using an external AI service
- Use the same core questions and scoring anchors for the same role
- Scores cite concrete answers, not “good feeling” or vague culture fit
- AI does not make the final hire, rejection, or compensation decision
- Candidate-data storage and retention follow company policy
- Rejection communication avoids unverified or potentially discriminatory reasons
10) Failure Modes and Fixes
| Failure | Cause | Fix |
|---|---|---|
| A match score looks objective | No shared scoring anchor | Define capabilities and 1/3/5 behaviour evidence first |
| AI prefers elite schools or brands | Input contains proxy variables | De-identify and require job-relevant evidence only |
| Many questions produce incomparable interviews | Every candidate gets a different core interview | Fix core questions; personalise only follow-ups |
| Summary sounds overly certain | Missing information is filled in | Separate verified, untested, and contradictory evidence |
11) Chapter Deliverable
Create a Structured Interview Kit: capability map, core questions, follow-up paths, scoring anchors, and evidence form. For candidate data, continue to AI Security and Ethics.