How to Evaluate AI-Assisted Coding Skills in Interviews?

Mar 15, 2026

The Problem

I was reviewing a take-home assignment from a senior developer candidate. The code was clean, well-structured, with comprehensive tests. On paper, perfect.

Then I asked a simple question: “Walk me through how you approached this.”

The candidate hesitated. “I mostly used Copilot and asked ChatGPT for the architecture. It took about two hours.”

Two hours for what should have been a full-day project. Impressive productivity. But when I dug deeper:

“Why did you choose PostgreSQL over MongoDB?” - “Copilot suggested it.”
“How does the rate limiting work?” - “I’m not sure, let me check the code.”
“What’s this caching strategy?” - “ChatGPT wrote that part.”

The candidate could explain what the code did, but not why it did it that way. They could debug the syntax, but not the architecture. They could use AI to build fast, but couldn’t evaluate if AI’s choices were right.

I realized I needed a new interview framework. Traditional coding interviews test syntax generation—the very thing AI handles best. What I needed to test was whether candidates could direct, validate, and refine AI output.

Why AI-Assisted Coding Skills Matter Now

AI coding assistants are standard tools. GitHub Copilot has over 1.5 million subscribers. Cursor raised $60 million. Claude Code handles complex refactoring across codebases.

The value has shifted. Syntax generation is commoditized. The skill gap is now in:

Decomposing problems for AI implementation
Writing effective prompts that yield quality output
Identifying when AI suggestions are wrong
Refactoring and improving AI-generated code

Engineers who can’t leverage AI effectively fall behind. But engineers who rely on AI without understanding create unmaintainable code.

The interview challenge: How do you separate these two types of candidates?

A Three-Pillar Framework

After iterating on interview processes with my team, I developed a framework focused on three areas that AI can’t replace:

Prompt Engineering Quality - Can they communicate technical requirements precisely?
Code Review Judgment - Can they catch subtle bugs in AI output?
Architectural Decision-Making - Can they explain trade-offs and choices?

Let me show you how each works in practice.

Pillar 1: Prompt Engineering Assessment

The quality of prompts reveals how candidates think. Poor prompts produce poor code. Good prompts show technical depth.

What Bad Prompts Look Like

I started asking candidates to share their prompts from take-home assignments. Here’s what raises red flags:

Write a user authentication system

This tells me nothing about:

Technology stack
Security requirements
Testing expectations
Architectural context
Quality standards

When I see this, I know the candidate let AI make all the decisions.

What Good Prompts Look Like

Here’s a prompt that shows skill:

Implement a user authentication system using FastAPI with:
- JWT tokens with 1-hour expiration
- Password hashing using bcrypt
- PostgreSQL storage via SQLAlchemy
- Rate limiting: 5 login attempts per minute per IP
- Email validation with regex
- Unit tests for login, logout, register, password reset
- Follow SOLID principles
- Include error handling for all edge cases

Context: This is for a microservices architecture. The auth service
will be called by other services through internal API calls.

This candidate demonstrates:

Technical specificity (FastAPI, JWT, bcrypt, SQLAlchemy)
Security awareness (rate limiting, password hashing)
Testing expectations (unit tests for specific functions)
Architectural context (microservices)
Quality standards (SOLID, error handling)

The Prompt Review Checklist

When I review prompts from take-home assignments, I use this checklist:

[ ] Prompts submitted with solution
[ ] Clear context provided
[ ] Specific constraints defined
[ ] Iteration visible in prompt history
[ ] Technology choices explained
[ ] Edge cases addressed
[ ] Testing requirements included
[ ] Security considerations mentioned

If a candidate doesn’t include prompts, I ask them to walk me through how they’d prompt for a specific feature. The conversation reveals the same information.

Pillar 2: Code Review of AI-Generated Output

The meta-skill for the AI era is knowing what to validate after an agent generates code.

I started asking candidates to review AI-generated PRs during interviews. You learn so much about someone’s depth by watching them catch—or miss—the subtle bugs that agents introduce.

The AI-PR Review Exercise

I present candidates with code that looks correct at first glance. Here’s an example I use:

async def process_user_data(user_id: str, data: dict):
    result = await db.query(f"SELECT * FROM users WHERE id = {user_id}")
    user = result[0]
    user['email'] = data['email']
    user['preferences'] = data.get('preferences', {})
    await db.save(user)
    return user

This code works. It passes tests. But I want to see what candidates identify.

What Strong Candidates Catch

Critical Issues (Must Identify):
- SQL injection vulnerability (string interpolation in query)
- No input validation (user_id, data)
- Missing error handling for missing keys

Important Issues (Should Identify):
- No rate limiting consideration
- No transaction handling
- Type safety concerns (dict access)

Bonus Points:
- Suggests parameterized queries
- Proposes input schema validation
- Mentions logging for audit trail

A senior candidate will catch the SQL injection immediately. A mid-level candidate might spot missing validation. A junior candidate might say “the code looks fine.”

A More Subtle Example

Here’s a TypeScript example that tests deeper understanding:

async function fetchUserData(ids: string[]) {
  const results = [];
  for (const id of ids) {
    const response = await fetch(`/api/users/${id}`);
    const data = await response.json();
    results.push(data);
  }
  return results;
}

This code works. But what does the candidate identify?

// Issues identified:
// 1. Sequential requests - should use Promise.all for parallel
// 2. No error handling - failed requests break the loop
// 3. No input validation - empty/null ids not handled
// 4. No rate limiting - could hit API limits
// 5. No timeout - requests could hang
// 6. No type safety - any[] return type

async function fetchUserData(ids: string[]): Promise<User[]> {
  // Input validation
  if (!ids?.length) return [];

  // Parallel requests with error handling
  const results = await Promise.allSettled(
    ids.map(id =>
      fetch(`/api/users/${id}`, {
        signal: AbortSignal.timeout(5000)
      })
      .then(res => {
        if (!res.ok) throw new Error(`Failed: ${res.status}`);
        return res.json();
      })
    )
  );

  // Filter successful results
  return results
    .filter((r): r is FulfilledPromise<User> => r.status === 'fulfilled')
    .map(r => r.value);
}

A candidate who spots parallelization opportunity, adds timeout handling, and improves type safety demonstrates deep understanding.

Scoring Rubric

Critical Issues (Must Identify):
- Security vulnerabilities: +3
- Data integrity risks: +3
- Breaking bugs: +3

Important Issues (Should Identify):
- Missing error handling: +2
- Type safety gaps: +2
- Performance concerns: +2

Bonus Points:
- SOLID principle discussion: +1
- Testing strategy: +1
- Maintainability improvements: +1

Pillar 3: Architectural Decision-Making

The first two pillars test technical depth. This pillar tests judgment and communication.

The “Why” Questions

After reviewing code, I ask questions that probe understanding:

“Why did you choose FastAPI over Flask?”
“What trade-offs did you consider for the caching strategy?”
“How would this scale to 10,000 concurrent users?”
“What’s your testing strategy for AI-generated code?”

The answers reveal whether candidates understand the architecture or just copied AI suggestions.

Red Flag Responses

- "Copilot suggested it"
- "That's what the AI generated"
- "I'm not sure, let me check the docs"
- "It just worked"

Good Responses

- "I chose FastAPI because async support is critical for our
  I/O-bound workload. Flask's sync model would create bottlenecks."

- "The caching strategy uses write-through because read latency
  is the priority. If consistency mattered more, I'd use write-back."

- "At 10,000 concurrent users, I'd add connection pooling, move
  to Redis for session state, and consider read replicas."

The Problem Decomposition Test

I give candidates a feature request and ask them to break it down for AI implementation:

Implement a real-time notification system that:
- Sends push notifications to mobile apps
- Handles 1000 events per second
- Guarantees delivery (no message loss)
- Supports message prioritization

What I look for:

[ ] Identifies components (queue, worker, push service)
[ ] Considers failure modes (what if push fails?)
[ ] Addresses scalability (how to handle spikes?)
[ ] Mentions testing strategy (how to test without real devices?)
[ ] Considers observability (how to monitor health?)
[ ] Breaks into incremental prompts (not one giant prompt)

A candidate who says “I’ll ask AI to build a notification system” fails. A candidate who decomposes into “First, I’d set up the queue infrastructure, then add the push service abstraction, then implement retry logic…” shows they understand the system.

A Sample Interview Flow

Here’s how I structure a 90-minute interview:

AI-Assisted Coding Skills Interview (90 minutes)

Part A: Prompt Review Discussion (20 minutes)
- Review submitted prompts from take-home
- Discuss iteration process
- Explore alternative approaches considered

Part B: Live AI-PR Review (30 minutes)
- Present AI-generated PR with subtle bugs
- Candidate identifies issues live
- Discuss fixes and improvements

Part C: Architecture Deep Dive (25 minutes)
- Technology choices and trade-offs
- System design discussion
- Engineering practices assessment

Part D: Scenario Problem-Solving (15 minutes)
- "Here's a feature request. How would you break this down
  for AI implementation?"
- Evaluate problem decomposition skills
- Assess communication clarity for AI instructions

What This Framework Reveals

After using this approach for several months, I’ve noticed patterns:

Candidates who rely too heavily on AI:

Can explain what code does, but not why
Miss subtle bugs in generated code
Struggle with “why” questions about architecture
Give vague answers about trade-offs

Candidates who leverage AI effectively:

Write specific, context-rich prompts
Quickly identify AI’s mistakes
Explain architectural decisions clearly
Know when to trust AI and when to verify

The best candidates:

Use AI for speed, but verify critical sections
Iterate on prompts when output is inadequate
Have strong mental models that AI augments, not replaces
Treat AI as a junior developer who needs supervision

A Practical Implementation Note

When I first tried this approach, I made the mistake of asking candidates to live-code with AI during interviews. That was a disaster. Candidates were nervous, AI was unpredictable, and we spent most of the time fighting tool setup.

What works better:

Take-home with prompts required - Candidates work in their comfort zone with their tools. Prompts reveal their thinking.
Review prompts together - Discuss why they wrote prompts that way. What alternatives did they consider?
Live code review of provided AI output - Remove the AI variability. Test their judgment on consistent examples.
Architecture discussion - Probes understanding beyond what AI can provide.

Summary

In this post, I showed a practical framework for evaluating AI-assisted coding skills in interviews. The key insight is that traditional coding interviews test syntax generation—the very thing AI handles best.

Instead, focus on three areas AI can’t replace:

Prompt Engineering - Can they communicate technical requirements precisely?
Code Review Judgment - Can they catch subtle bugs in AI output?
Architectural Decision-Making - Can they explain trade-offs and choices?

Companies that implement structured evaluation of AI-assisted coding skills will build teams capable of leveraging these powerful tools while maintaining code quality, security, and maintainability standards.

The candidates who thrive in this environment aren’t the ones who use AI the most—they’re the ones who use AI wisely, verify thoroughly, and understand deeply.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit: How do you evaluate candidates who use AI coding tools?
👨‍💻 GitHub Copilot Best Practices
👨‍💻 Claude Code Documentation

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!