A well-designed AI interview is the difference between noisy data and clear hiring signals.
Start with the End in Mind
Before configuring your interview, answer these questions:
- What must this person be able to do on Day 1? These are your non-negotiable criteria and should carry the highest weight.
- What skills can be learned on the job? These are nice-to-haves — include them at lower weight or leave them out entirely.
- What does failure in this role look like? This helps you identify red flags and design questions that surface them early.
Structure Your Interview
A good AI interview follows a natural flow that builds comfort and progressively goes deeper:
| Phase | Purpose | Duration | Example Questions |
|---|
| Warm-up | Help the candidate settle in and feel comfortable | 2–3 min | ”Tell me about yourself and what drew you to this role.” |
| Experience deep-dive | Explore relevant background and achievements | 5–10 min | ”Walk me through a project you led that you’re most proud of.” |
| Skills assessment | Role-specific technical or competency questions | 5–10 min | ”How would you approach designing a data pipeline for real-time event processing?” |
| Scenario questions | Test judgment and problem-solving | 3–5 min | ”A key stakeholder disagrees with your approach mid-project. How do you handle it?” |
| Candidate questions | Let the candidate ask about the role and company | 2–3 min | Open-ended |
The warm-up isn’t filler — it’s strategic. Candidates who feel comfortable give better, more authentic answers. Skipping it leads to guarded, surface-level responses throughout the entire interview.
Sample Agendas by Role Type
Software Engineer (Mid-Level) — 20 minutes
| Topic | Weight | What to Assess |
|---|
| Technical problem-solving | 30% | Approach to debugging, system design thinking |
| Relevant experience | 25% | Past projects, technologies used, scale |
| Collaboration & communication | 20% | How they work with PMs, designers, cross-functional teams |
| Learning agility | 15% | How they pick up new tools, handle unfamiliar problems |
| Motivation & cultural fit | 10% | Why this company, what they’re looking for |
Sales Representative — 15 minutes
| Topic | Weight | What to Assess |
|---|
| Sales methodology & process | 30% | How they prospect, qualify, close |
| Communication skills | 25% | Clarity, persuasiveness, active listening |
| Resilience & drive | 20% | How they handle rejection, long sales cycles |
| Product understanding | 15% | Ability to learn and articulate product value |
| Cultural fit | 10% | Team dynamics, work style |
Customer Support Lead — 15 minutes
| Topic | Weight | What to Assess |
|---|
| Problem resolution | 30% | Approach to escalations, troubleshooting methodology |
| Empathy & communication | 25% | Tone, patience, ability to de-escalate |
| Leadership experience | 20% | Managing a team, coaching, handling underperformance |
| Technical aptitude | 15% | Comfort with tools, ability to learn new systems |
| Process improvement | 10% | Identifying patterns, suggesting improvements |
Calibrating Follow-Up Depth
Hello Recruiter’s AI asks follow-up questions based on candidate responses. You can control how deep it goes:
- Light follow-ups — Good for high-volume, entry-level roles where you need quick screening. The AI accepts answers at face value and moves on.
- Moderate follow-ups — The default. The AI asks one follow-up per topic to get more specific examples.
- Deep follow-ups — Best for senior or specialized roles. The AI probes multiple layers — asking for specifics, challenging assumptions, and requesting alternative approaches.
Match depth to seniority. Deep follow-ups on an entry-level role will feel like an interrogation. Light follow-ups on a VP role will miss critical insights.
Common Mistakes to Avoid
Don’t overload with criteria. 5–7 evaluation criteria is the sweet spot. More than that dilutes the signal — each criterion contributes so little to the overall score that it becomes hard to differentiate candidates meaningfully.
Don’t copy generic criteria from job description templates. “Strong communication skills” and “team player” are so vague they’re almost meaningless. Be specific: “Ability to explain technical trade-offs to non-technical stakeholders” is far more useful to the AI evaluator.
Test your own interview before going live. Complete the full AI interview yourself. You’ll immediately spot questions that are confusing, topics that feel redundant, or a total duration that’s too long. If you wouldn’t want to sit through it, your candidates won’t either.
