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Jan 27, 2026

Designing AI-Resistant Interview Questions – Practical Alternatives to Algorithmic Coding Tests

LeetCode tests are broken. 80% of candidates now use AI on coding assessments, and Claude Opus 4.5 solves most standard problems instantly. You need alternatives that check genuine problem-solving, communication, and engineering judgement.

This guide gives you templates for four formats that resist AI: architecture interviews, debugging scenarios, code reviews, and collaborative coding. You’ll get question templates, rubrics, and a roadmap to shift from LeetCode to company-specific questions. This practical implementation guide is part of our strategic framework for interview redesign, helping you transition from vulnerable algorithmic tests to formats that assess real engineering capabilities.

What Makes Interview Questions AI-Resistant?

AI-resistant questions evaluate process over output. How candidates think through constraints and adapt to changes matters more than whether they produce correct code.

Contextual understanding creates resistance. Ask candidates about your tech stack, scale limits, or budget. You introduce information AI models never saw during training.

Iterative refinement works because AI struggles with real back-and-forth. Introduce new constraints, ask candidates to defend decisions, force real-time adaptation.

Communication skills reveal understanding. Explaining choices, diagramming systems, collaborating in real-time—these expose whether candidates grasp their solutions or just regurgitate AI output.

Out-of-distribution problems resist AI because models haven’t seen similar patterns. Use novel scenarios, company-specific contexts, unusual constraints.

Multi-dimensional evaluation creates layered defence. Check technical ability AND communication, debugging methodology, architectural thinking, collaboration. No AI model excels across all dimensions at once.

How Do Architecture Interviews Resist AI Assistance?

Architecture interviews evaluate system design through open-ended problems. Candidates diagram solutions and discuss trade-offs using whiteboards or tools. The format resists AI through visual thinking and real-time dialogue.

The diagram-constrain-solve-repeat pattern forms the core. Present a scenario, ask for a diagram, then introduce constraints: “The database is overloaded—how do you address this?” or “Budget is cut 50%—what changes?” Each round reveals how candidates think through trade-offs.

Sessions run 45-60 minutes with 2-3 constraint rounds. You observe thought process, not just evaluate final design.

Architecture Interview Template

Opening (5 min): Present realistic challenge. Example: “Design a social feed ranking system for 100,000 daily active users.”

Initial Diagramming (15 min): Ask candidate to diagram proposed architecture using standard conventions.

Constraint Progression (20-25 min): Introduce 2-3 constraints:

Constraint 1 (Scalability): “User base grows to 10 million—where are bottlenecks?”
Constraint 2 (Feature): “Product wants real-time personalisation based on last 60 seconds—how does architecture change?”
Constraint 3 (Business): “Budget cut 40%—what trade-offs?”

Evaluation (5-10 min): Ask candidate to reflect on choices, identify weaknesses, explain what they’d optimise first.

Architecture Interview Rubric

Suggested Weighting: Initial Design (15%), Adaptation (25%), Trade-offs (25%), Technical Depth (20%), Communication (15%).

How Do You Create Debugging Scenarios?

Present candidates with production issues from your systems: performance bottlenecks, intermittent failures, resource constraints. The format evaluates systematic debugging, not memorised algorithms.

Start with sanitised production incidents from post-mortems. Remove sensitive details but keep technical context and constraints.

Use progressive information disclosure: candidates ask questions to gather context, simulating real debugging where information isn’t immediately available.

Focus on methodology (systematic vs random), communication (explaining theories, asking questions), and practical sense (knowing what to investigate first).

Debugging Scenario Template

Initial Symptoms: Present observable problem. “API response times degraded from 200ms to 3+ seconds for 20% of requests. Users report intermittent timeouts. No deployments occurred.”

System Context: Provide architecture diagram: API gateway, application servers, PostgreSQL, Redis, RabbitMQ. Include scale: 50,000 requests/day, 200GB database, 3 app servers.

Available Tools:

Application logs (request/response times)
Database query logs
Server metrics (CPU, memory, disk I/O)
Network latency measurements
Cache hit/miss rates

Progressive Disclosure: Don’t provide everything upfront. When candidates ask specific questions, reveal relevant details.

Expected Path:

Form hypothesis about causes
Ask targeted questions for evidence
Identify root cause
Propose solution

Debugging Rubric

Testing AI Resistance

Before deploying, test scenarios with AI. Provide only initial symptoms to Claude/ChatGPT. If it proposes complete solution without asking questions, add more ambiguity. Effective scenarios require 3-4 back-and-forth exchanges to reach solution.

What Structure Works for Code Review Sessions?

Present candidates with existing code (500-1,000 lines) containing bugs, architectural issues, or improvement opportunities. Ask them to critique, explain problems, suggest fixes. This reveals how they’d contribute to actual team code reviews.

Use multi-round format: initial review (15-20 min), findings discussion (15-20 min), deeper dive on selected issues (10-15 min).

Questions check code comprehension, critical thinking (identifying real vs superficial issues), communication, and practical judgement (prioritising important issues).

Code samples should come from your company’s domain with realistic business context, making generic AI critiques less effective.

Code Review Session Template

Phase 1: Independent Review (15-20 minutes)

Provide code via shared screen
Ask candidate to review as if it’s a teammate’s pull request
Candidate documents findings
No interruptions during review

Phase 2: Findings Discussion (20-25 minutes)

Candidate presents findings
Probe with questions: “Why is this a problem?” “How would you prioritise fixes?” “What’s the business impact?”
Discuss 3-4 most significant issues

Phase 3: Improvement Proposal (10-15 minutes)

Select one architectural issue
Ask for sketched improved design
Discuss trade-offs

Code Review Example: Payment Processing

class PaymentProcessor:
    def __init__(self):
        self.payments = []
        self.db = connect_to_database()

    def process_payment(self, user_id, amount, card_token):
        try:
            result = charge_card(card_token, amount)
        except:
            return False

        payment_id = self.db.insert({
            'user_id': user_id,
            'amount': amount,
            'status': 'completed',
            'timestamp': datetime.now()
        })

        user = self.db.users.find(user_id)
        user.balance = user.balance + amount
        self.db.users.save(user)

        send_email(user.email, 'Payment processed: ${}'.format(amount))
        self.payments.append(payment_id)
        return True

Seeded Issues:

No transaction atomicity—payment charged but database insert could fail
Bare except swallows all errors including KeyboardInterrupt
Card token stored in database (PCI violation)
Race condition in balance update
No rollback if email fails
Tight coupling between payment and email
No logging
Hard to test

Code Review Rubric

How Do Collaborative Coding Embrace AI Tools?

Collaborative coding simulates pair programming, explicitly allowing AI tools (Claude, Copilot, ChatGPT) whilst evaluating how candidates use them. This counterintuitive approach increases resistance by making candidate behaviour the signal.

Format focuses on problem-solving partnership: candidates explain thinking, discuss trade-offs, use AI as a tool not a crutch, demonstrate judgement about when AI suggestions work.

Sessions run 60-90 minutes with ambitious scope requiring multiple components and integration—more than AI can auto-generate but achievable for candidates using AI strategically.

Assessment shifts from “can you code without AI?” to “can you lead solutions using AI as one tool?”—checking architectural thinking, debugging when AI fails, code review of AI output, communication.

AI resistance increases when you allow AI because candidate behaviour becomes the signal. How they prompt AI, when they override suggestions, how they explain decisions—these reveal engineering maturity AI can’t simulate. Learn more about how Canva and Google redesigned technical interviews to implement this philosophy.

Collaborative Coding Template

Pre-Interview Communication (24 Hours Before): “Tomorrow’s interview is collaborative coding. Work with your interviewer to build a feature. You’re explicitly encouraged to use AI coding assistants (Claude, Copilot, ChatGPT) just as you would on the job. We’re evaluating problem-solving approach, architectural thinking, and ability to use AI effectively—not syntax memorisation.”

Session Structure (75 minutes)

Phase 1: Problem Introduction (10 min)

Present realistic feature with business context
Discuss requirements, clarify ambiguities
Establish technical constraints explicitly

Phase 2: Solution Design (15 min)

Candidate outlines architectural approach
Discuss design trade-offs before coding
Probe: “How will this scale?” “What could go wrong?”

Phase 3: Collaborative Implementation (40 min)

Candidate implements using AI tools as desired
Interviewer acts as pair programmer
Focus on decision-making: when they use AI, how they review code, how they debug

Phase 4: Reflection (10 min)

Discuss what went well and improvements
Ask about AI usage: when helpful, when less so?
Probe understanding: “Explain how this works” “What if X fails?”

Example: Rate Limiting API

Context: API has no rate limiting. Individual users accidentally overwhelm system. Product wants: max 100 requests per user per minute, clear error messages when limits exceeded.

Constraints:

Python Flask API
Redis available
Preserve state across restarts
Need monitoring metrics
Toggleable via feature flag

Success Criteria:

Middleware limits requests per user per minute
Returns HTTP 429 with helpful message
Includes reset time in headers
Redis keys expire appropriately
Basic tests demonstrating functionality

Collaborative Coding Rubric

Ground Rules

What’s Allowed:

All AI assistants
Documentation lookups
Normal dev tools
Asking interviewer questions

What We Evaluate:

Problem breakdown
Judgement about when/how to use AI
Ability to review and improve AI code
Debugging skills when things fail
Communication and collaboration
Architectural decision-making

What Principles Guide Custom Question Development?

Start with your company’s actual challenges: specific tech stack problems, domain-specific constraints, or novel requirement combinations not in public question banks.

The out-of-distribution principle forms the foundation. Create problems AI models unlikely encountered during training by combining unusual constraints, using obscure contexts, or requiring understanding of proprietary systems. This addresses why LeetCode interviews fail to assess real capabilities—they test memorised patterns rather than contextual problem-solving.

Apply progressive complexity: questions start accessible but branch into deeper challenges through interviewer-added constraints.

Include context requirements: embed problems in realistic business scenarios (scale, budget, team constraints) requiring practical judgement not just algorithmic correctness.

Test questions with AI tools before using them. Verify Claude or ChatGPT can’t solve without additional context or iterative guidance you’ll provide during interview.

Six-Step Custom Question Framework

Step 1: Identify Authentic Problem Source from actual engineering work:

Recent production incidents
Architectural decisions made
Performance optimisations implemented
Code review discussions
Technical debt decisions

Step 2: Sanitise and Generalise Remove sensitive details whilst preserving complexity:

Strip proprietary data
Keep realistic constraints and context
Maintain interesting trade-offs

Step 3: Add Progressive Constraints Design 2-4 constraint levels:

Level 1: Baseline accessible to all
Level 2: Scalability/performance constraint
Level 3: Business reality (budget, timeline, team)
Level 4: Additional complexity (regulatory, security)

Step 4: Define Evaluation Criteria Create rubric covering:

Technical correctness
Problem-solving process
Communication and collaboration
Practical judgement
Depth of understanding

Step 5: Test with AI Tools Verify AI resistance:

Provide to Claude/ChatGPT without context
If AI solves completely, add contextual complexity
Iterate until requires human judgement

Step 6: Pilot and Iterate Test with internal volunteers:

Run with 3-5 team members
Calibrate difficulty and timing
Refine rubric based on feedback

AI-Resistance Testing Checklist

Uniqueness:

[ ] Problem doesn’t appear in LeetCode
[ ] Constraints are novel combinations
[ ] Domain context is company-specific

Context Requirements:

[ ] Requires understanding business constraints
[ ] Technical context gathered through questions
[ ] Production realities influence solution

Iterative Nature:

[ ] Multiple rounds of constraint introduction
[ ] Candidate adapts based on new information
[ ] Discussion reveals thinking process

Multi-Dimensional Assessment:

[ ] Tests technical ability AND communication
[ ] Evaluates process, not just correctness
[ ] Assesses practical judgement
[ ] Reveals understanding through explanation

AI Tool Testing:

[ ] Tested with Claude Opus 4.5 or ChatGPT
[ ] AI can’t solve without additional context
[ ] AI solutions have gaps requiring human judgement

How Do You Transition from LeetCode?

Begin with parallel testing: run new formats alongside LeetCode for 3-6 months, comparing assessments and hire quality.

Implement phased rollout by stage: start with final-round architecture interviews whilst keeping phone screens algorithmic, gradually expanding AI-resistant formats.

Invest in interviewer training: new formats require different facilitation skills versus traditional coding interviews.

Build question bank of 15-20 questions per format before launching. Plan for each question to be used maximum 2-3 times before rotation to prevent sharing.

Establish calibration sessions where interviewers practice together, discuss scoring, build shared understanding.

Plan for 4-6 month transition: rushing creates uncertainty, prolonging creates confusion.

12-Week Transition Roadmap

Phase 1: Preparation (Weeks 1-4)

Week 1: Format selection and question sourcing Week 2: Question development (8-10 per format) Week 3: Interviewer training preparation Week 4: Initial training and pilot setup

Phase 2: Pilot Testing (Weeks 5-8)

Weeks 5-6: Parallel format testing Week 7: Mid-pilot review and calibration Week 8: Pilot data analysis and go/no-go decision

Phase 3: Full Rollout (Weeks 9-12)

Week 9: Rollout planning and communication Weeks 10-11: Graduated rollout (replace one round at a time) Week 12: Complete transition and documentation

Interviewer Training Curriculum

Session 1: Introduction (2 hours)

Why traditional interviews fail with AI
Overview of alternative formats
Philosophy shift: process over correctness

Session 2: Format Deep Dive (3 hours)

Detailed training on each format
Example questions and rubrics
Live demonstrations
Practice facilitation

Session 3: Calibration Workshop (2 hours)

Review recorded interviews together
Discuss scoring decisions
Align on rubric interpretation

Session 4: Certification (Variable)

Each interviewer conducts 2-3 supervised interviews
Receive feedback on facilitation
Achieve certification before conducting independently

How Do You Measure Success?

Track hire quality: performance review scores, promotion rates, retention at 12/24 months, comparing pre- and post-transition cohorts.

Monitor interviewer confidence: quarterly surveys on assessment confidence, AI cheating concerns, format effectiveness.

Measure candidate experience: post-interview surveys on fairness perception, process clarity, format preference.

Analyse false positive/negative indicators: offers declined by strong candidates, struggling new hires who interviewed well.

Assess consistency: inter-interviewer agreement on scores, rubric utilisation rates, scoring variance.

Effectiveness Metrics Dashboard

Leading Indicators (Monthly)

| Metric | Target | Status | |——–|——–|——–| | Interview Completion Rate | 85%+ | 🟢/🟡/🔴 | | Interviewer Confidence | 4.0+/5.0 | 🟢/🟡/🔴 | | Candidate Experience NPS | +30 or better | 🟢/🟡/🔴 | | Scoring Consistency | 80%+ agreement | 🟢/🟡/🔴 |

Lagging Indicators (Quarterly/Annually)

| Metric | Pre-Transition | Post-Transition | Change | |——–|—————|—————–|——–| | 12-Month Performance | X.X/5.0 | X.X/5.0 | +/- X% | | 12-Month Retention | X% | X% | +/- X% | | 24-Month Retention | X% | X% | +/- X% | | Promotion Rate | X% | X% | +/- X% |

FAQ

What makes a good architecture question for AI resistance? Combine realistic business constraints, progressive difficulty through added limitations, and emphasis on trade-off discussions rather than single correct solutions. The diagram-constrain-solve-repeat pattern proves particularly effective.

Can AI solve debugging scenarios if given enough context? AI struggles with scenarios requiring investigative process (forming hypotheses, knowing what to check first), domain-specific knowledge about your systems, and practical judgement—especially when information is progressively disclosed through questions.

Should we allow AI during collaborative coding interviews? Yes. Explicitly allowing AI lets you assess realistic job performance: how candidates use AI effectively, review its output, debug when it fails, and demonstrate engineering judgement. Candidate behaviour becomes the evaluation signal.

How many custom questions do we need? Build 15-20 questions per format before full transition to ensure rotation without repetition. You can pilot with fewer (8-10) but need more for production scale.

How long does interviewer training take? Plan 4-6 weeks including format introduction (2 hours), practice interviews (2-3 rounds), calibration discussions (2 sessions), and shadowing (2-3 interviews)—approximately 12-16 hours per interviewer. Rushed training leads to inconsistent evaluation.

What if candidates complain about unfamiliar formats? Provide clear advance communication about format, rationale, and preparation guidance. Most prefer realistic assessments over abstract puzzles when expectations are properly set. Send detailed materials 48 hours before interviews.

Do these work remotely? Yes. All formats work effectively remote with appropriate tools: virtual whiteboards (Miro, Excalidraw), screenshare, collaborative editors (VS Code Live Share). Remote may be more AI-resistant because you observe screen sharing in real-time.

How do we prevent questions being shared? Accept some sharing will happen but mitigate through question rotation (retire after 6-12 months), progressive constraints (can’t be fully documented), contextual requirements (requires interviewer interaction), and continuous development.

Can code review work for senior roles? Yes—especially well. Use more complex codebases, focus on architectural critique versus bug-finding, and assess mentoring communication through how they explain improvements. Seniors should identify systemic issues, not just bugs.

Conclusion

Transitioning from LeetCode requires investment: question development, interviewer training, process refinement. But with 80% of candidates using AI assistance, traditional formats no longer serve their purpose.

The frameworks here—architecture interviews, debugging scenarios, code reviews, collaborative coding—provide actionable alternatives proven by companies like Anthropic, WorkOS, Canva, and Shopify.

Start now: Test your current questions with Claude Opus 4.5. Select one format and develop 8-10 questions. Train 3-5 interviewers and pilot with 10-20 candidates. Build from actual technical challenges your team faces.

“AI-proof” remains impossible but “AI-resistant” proves achievable through layered defences. The question isn’t whether AI will advance—it will. The question is whether your interviews evolve to assess human capabilities that remain valuable: judgement, communication, contextual problem-solving, and collaborative thinking.

For a comprehensive overview of how these alternatives fit into your overall response strategy—including detection and embrace approaches—see our complete guide to navigating the AI interview crisis.