Unstructured interviews are where hiring goes to guess: every interviewer asks different questions, grades on vibes, and the loudest opinion wins the debrief. This playbook builds a competency-mapped question bank with scoring anchors for each role — so every candidate faces the same fair interview and every score means the same thing.
Workflow area: HR & Recruiting · Tier: Core · Hands-on time: ~35 minutes
Structured interviews are one of the best-evidenced predictors of job performance — far ahead of free-form conversations — yet most teams skip them because writing good behavioral questions with scoring anchors is tedious. That tedium is exactly the work AI does well. Your job is to define the competencies (that's a hiring decision) and pressure-test the anchors; the model drafts the fifty questions you'd never find time to write.
Fix the competencies first — humans only. From the JD and the hiring manager, agree on 4–6 competencies, each with one sentence on what it means in this role. Example for a customer support lead: case-handling judgment, coaching ability, process improvement, stakeholder communication, composure under load.
Generate the bank, one competency at a time.
Create interview questions for this competency. Role and context:
Role: [TITLE, LEVEL, TEAM]
Competency: [NAME] — [YOUR ONE-SENTENCE DEFINITION]
What good looks like in this role: [1-2 CONCRETE EXAMPLES]
Produce:
1. Three BEHAVIORAL questions ("Tell me about a time...") that
probe past evidence of this competency
2. Two SITUATIONAL questions ("What would you do if...") using
realistic scenarios from our context: [ONE LINE OF CONTEXT,
e.g. "B2B SaaS support, 4-person team, 200 tickets/week"]
3. For each question: two follow-up probes an interviewer can use
when the answer stays vague
4. SCORING ANCHORS on a 1-4 scale for the competency:
1 = concerning, 2 = below bar, 4 = strong — describe the
OBSERVABLE evidence at each level, not adjectives.
Rules: no brainteasers, no trick questions, no questions about
protected characteristics or anything adjacent (family plans,
age, health, religion, nationality). Questions must be answerable
by candidates from any company background, not just our industry.
Pressure-test the anchors — this is where quality lives. A weak anchor says "2 = poor communication." A usable one is observable:
Competency: Coaching ability — scoring anchors
1 — Blames team members when describing failures; no example of
developing anyone; probes reveal they did the work themselves
instead of coaching.
2 — Gives feedback examples but only corrective and ad hoc; no
evidence of a development plan or follow-through.
3 — Concrete example of diagnosing a skill gap, agreeing a plan,
and following up; outcome stated.
4 — Multiple examples across different people; adapts approach per
person; can cite a measurable improvement they coached
someone to.
Ask the model: "For each question, what would a well-rehearsed but weak candidate say that could fool an interviewer? Add a probe that exposes it." Then delete any question you couldn't answer well yourself from a past role — that's the realism test.
Run the legal and fairness sweep.
Review the full bank. Flag any question that: could elicit
protected-characteristic information even indirectly; assumes a
traditional career path; requires unpaid work ("design us a
plan"); or is culturally specific to one region. Suggest a
compliant rewrite for each flag.
Treat the output as a checklist for your HR/legal review, not a substitute for it.
Assemble the interviewer kit in Google Sheets. One row per question: competency, question, follow-up probes, anchors 1–4, and a blank evidence-notes column. Add a cover row with the rules: same core questions per candidate, score independently before the debrief, evidence quotes required for any 1 or 4.
Never paste candidate names, resumes, or interview responses into this workflow — the bank is built before candidates exist, from role requirements only. Evaluating actual candidates with AI raises legal obligations in many jurisdictions (and is restricted under several AI regulations); keep scoring human.
Structure is the fairness mechanism: same questions, same anchors, independent scores. AI makes structure cheap to build — it takes an afternoon instead of a quarter — but the competencies and every hiring judgment remain yours.