AI-Assisted Coding Interviews: How to Assess Engineers Who Use AI Tools

A candidate generates a working endpoint in six minutes. The code looks clean, the tests pass, and the demo runs. Then you ask what happens if the same request arrives twice.

They turn back to the AI assistant.

That response could lead to a useful investigation or another patch they cannot explain. The distinction matters when you are deciding whether this person can own the code after the interview ends.

An AI-assisted coding interview allows candidates to use AI tools during a technical exercise. A useful assessment examines how they define the problem, inspect suggestions, test behavior, and handle mistakes. The finished implementation gives you evidence. Their decisions tell you how much confidence to place in it.

Here is how to structure the interview, choose an exercise, and evaluate candidates consistently.

Key Takeaways

• An AI-assisted coding interview assesses how an engineer solves problems with AI tools available.
• Set clear rules and provide comparable access to tools, documentation, and development environments.
• Choose a bounded task that reflects decisions the engineer will face in the role.
• Evaluate problem framing, code comprehension, verification, debugging, and ownership.
• Use a relevant failure case or requirement change to explore whether the candidate understands the implementation.
• Record specific observations before comparing candidates.

What Is an AI-Assisted Coding Interview?

An AI-assisted coding interview is a technical assessment in which candidates can use tools that generate, explain, review, or modify code. The setup might include an editor assistant, a chat interface, or a coding agent working in a supplied repository.

The interviewer evaluates both the work and the reasoning behind it.

Useful questions include:

• Does the candidate clarify important requirements?
• Can they recognize unsupported assumptions in generated code?
• Do they understand the changes they accept?
• Can they test whether the implementation meets the brief?
• When something fails, can they investigate the cause?
• Can they explain what remains incomplete?

The format is especially relevant when engineers will have access to similar tools at work. Its value depends on the exercise, however. A task that an assistant can complete with little candidate involvement may leave you with an impressive artifact and few useful hiring signals.

Define What the Interview Needs to Reveal

Start with the work the engineer will own.

A backend role may require decisions about retries, data consistency, and API behavior. A frontend role may involve accessibility, state management, and incomplete responses. An infrastructure role may require someone to diagnose a failed deployment while limiting disruption.

Choose an exercise that exposes those decisions.

For a startup hiring a founding engineer, that might mean assessing how the candidate narrows an ambiguous request, chooses a workable approach, and explains the shortcuts they took. An engineer joining an established platform team may need a deeper discussion about compatibility and operational risk.

Write down three or four capabilities the session must reveal before drafting the task.

“Strong technical skills” is too broad.

“Recognizes that retrying a request can repeat a business action and proposes a workable way to prevent it” gives interviewers something observable.

Apply the same principle to specialist roles. When hiring AI agent developers, an exercise could explore tool failures, state management, or the decision to replace an autonomous step with a deterministic workflow.

The assessment should follow the responsibilities of the role closely enough that strong performance means something beyond the interview.

Establish Clear Rules for AI Use

Send the policy before the session. Candidates need time to understand the format and check their setup.

Explain:

• Which AI tools are available or permitted.
• Whether candidates can use their own accounts and editor configuration.
• Whether documentation and internet search are allowed.
• Which materials can be submitted to an external tool.
• What visibility the interviewer needs into generated changes.
• How tool outages will affect the session.
• Which capabilities the team will assess.

Where possible, provide a shared environment and a short setup period. Different subscriptions, models, and configurations can affect what candidates finish.

If you allow different tools, document that choice and be cautious about comparing completion speed.

A simple policy could read:

You may use the AI assistant provided in the interview environment, along with official documentation. Keep generated changes visible and be prepared to explain the code you accept. We will assess your reasoning, verification, debugging, and implementation decisions. You do not need to narrate every keystroke. The repository contains synthetic data supplied for this exercise.

Keep disclosure practical. Candidates should know what they can use and what they need to explain without having to guess the interviewer’s expectations.

Choose a Task With Meaningful Decisions

A useful exercise is small enough to understand quickly and substantial enough to support a technical conversation.

An existing repository often provides a good starting point. Candidates can inspect the code, make a focused change, and verify its behavior without spending most of the session on setup.

Task type	Example	What to assess
Extend a feature	Add filtering to an existing API	Requirements, integration, compatibility
Fix a defect	Investigate duplicate order creation	Debugging, data flow, verification
Review a patch	Inspect generated code containing a subtle error	Comprehension, review quality, prioritization
Improve reliability	Handle a dependency that sometimes times out	Failure handling, testing, tradeoffs
Change a requirement	Add support for partial updates	Understanding, adaptability, scope control

Provide a working test command, clear setup instructions, and enough context to begin.

If a framework is incidental to the role, allow documentation. Recalling an obscure configuration option may contribute little to the hiring decision.

Also decide how much the candidate needs to finish. A session designed to assess debugging should leave enough time to investigate a failure. Filling the entire hour with implementation can prevent you from observing the capability you intended to test.

Example Exercise: Prevent Duplicate Orders

For a backend role, give the candidate a small order service with a working endpoint, a local datastore, and a basic test suite.

The brief

Clients sometimes retry order requests after a timeout. Update the service so that repeating a request with the same idempotency key does not create another order. Explain how you would verify the behavior and what limitations remain.

Keep the environment focused. The exercise does not need payment processing, deployment work, or external credentials.

Before interviewing, identify the required behavior and prepare a consistent set of follow-up scenarios.

Watch how the candidate frames the problem

Relevant questions might include:

• Where does the idempotency key come from?
• Is the key unique across all customers or within one account?
• What happens if the same key arrives with a different payload?
• Should a retry return the original response?
• Could matching requests arrive simultaneously?
• How long should the service retain the key?

A candidate does not need to ask every question immediately. Look for whether they identify assumptions that affect the implementation.

If they start coding quickly, ask them to explain their assumptions at a natural checkpoint.

Inspect how they handle a plausible generated solution

Suppose the assistant proposes checking whether the key exists, then creating an order if it does not.

That can work in a sequential test. Concurrent requests raise another question: could both requests complete the check before either creates the order?

The candidate might recognize the race condition, inspect the datastore’s guarantees, and consider an atomic operation or an appropriate uniqueness constraint.

They may be able to implement the fix during the session. They may instead explain what the local environment cannot demonstrate. Record that distinction.

The useful evidence is whether they connect a specific failure mode to a technically sound response.

Introduce a consistent follow-up

Ask:

“Two requests with the same key arrive almost simultaneously. Walk me through what happens in your implementation.”

Then:

“What happens if a later request uses that key with a different order payload?”

These questions explore whether the candidate understands the behavior they have built. They also reveal how they respond when the assistant’s first answer is incomplete.

Structure the Interview Around Five Stages

A 60-minute session can provide enough room for implementation and review if the exercise is appropriately scoped.

Stage	Suggested time	Purpose
Orientation	5 minutes	Confirm rules and check the environment
Problem framing	10 minutes	Explore assumptions and the initial approach
Implementation	20 minutes	Observe inspection, coding, and tool use
Verification and follow-up	15 minutes	Examine tests, failures, and a changed condition
Review	10 minutes	Discuss limitations, ownership, and next steps

1. Check the environment

Confirm that the repository runs and the permitted tools are available.

Resolve setup problems before treating the session as an assessment. A broken dependency should not quietly consume the candidate’s implementation time.

2. Ask for an initial approach

Before substantial changes, ask:

“How would you break this down, and what would you check first?”

Their answer gives you a reference point. You can observe whether they revise their approach as they learn more.

3. Allow focused implementation time

Let the candidate work without constant interruption.

Pause at meaningful decisions: accepting a large generated patch, introducing a dependency, changing a data model, or removing a test.

Ask why they made the change and what they expect it to do.

4. Explore verification

Ask the candidate to demonstrate the behavior, then introduce a relevant failure case.

If implementation time runs out, they can still write or describe the next test. Distinguish behavior they demonstrated from behavior they only discussed.

5. Finish with a handoff

Ask for a short review:

• What works?
• What remains uncertain?
• What would you change before release?
• What should the next engineer know?

An accurate account of incomplete work is useful evidence of ownership.

Keep each stage focused on a distinct question. Teams trying to hire engineers more quickly can use this structure to remove overlapping assessments while preserving time for the decisions that matter.

What Should You Evaluate?

The finished code may contain substantial AI assistance. Your assessment needs to establish what the candidate understood, verified, and decided.

Problem framing

Look for relevant questions, explicit assumptions, and a reasonable definition of success.

A detailed prompt can reveal sound thinking when it captures the actual requirements. Its length or polish is not a reliable measure on its own.

A candidate who writes a short prompt after carefully inspecting the repository may have done more useful work than someone who immediately submits the entire brief.

Code comprehension

Ask the candidate to explain a small but important section.

For the order service:

“What prevents a second order from being created here?”

Then change a condition:

“Would that still hold if the first request failed halfway through?”

Good explanations connect the code to its behavior. Follow up when an answer repeats a comment without explaining the underlying mechanism.

Verification

Look for tests derived from requirements and plausible failures.

For the order exercise, relevant cases could include:

• Repeated requests with the same key.
• Separate requests with different keys.
• A reused key with a conflicting payload.
• A failure during order creation.
• Concurrent requests, where the environment supports a meaningful test.

Generated tests also need review. They can pass while checking the wrong behavior or repeating a mistaken assumption.

Ask:

“If this implementation were wrong, which test would catch it?”

That question helps reveal whether the candidate understands what the suite establishes.

Debugging

When something fails, observe how the candidate investigates.

Do they inspect the error, form a hypothesis, and check it? Do they review a generated patch before rerunning the tests? Can they explain why the change addresses the cause?

Repeatedly asking an assistant to fix a problem can produce a working result. Without investigation or review, it leaves the candidate’s debugging ability uncertain.

Technical judgment

Ask why they chose the approach and what they left out.

A small implementation with clear limitations may fit the task well. A more elaborate solution may also be justified. Evaluate the relationship between the choices, requirements, time available, and maintenance burden.

For senior candidates, explore consequences beyond the immediate patch:

“What would make this difficult to operate six months from now?”

A useful answer should refer to the system in front of them.

Use a Scorecard That Captures Evidence

Keep the evaluation focused enough to complete immediately after the session.

Area	Strong evidence	Concern to investigate
Problem framing	Identifies consequential constraints and states assumptions	Begins implementation with a central ambiguity unresolved
Code comprehension	Explains critical changes and predicts behavior	Cannot explain an important generated function
Verification	Tests requirements and relevant failures	Relies on a single successful demonstration
Debugging	Investigates the cause and checks the fix	Accepts repeated patches without reviewing them
Judgment and ownership	Explains scope, tradeoffs, and remaining risks	Describes incomplete work as ready to release

Record concrete observations before discussing the candidate.

“Good with AI” gives the hiring team little to examine.

“Identified a race condition in the generated patch, explained why sequential tests missed it, and proposed a datastore constraint” supports a more useful discussion.

Connect the session to your broader AI engineer hiring scorecard. Practical exercises, project discussions, and other interviews should contribute complementary evidence.

Avoid allowing one polished demo to influence every rating.

What If the Candidate Finishes Early?

Use the remaining time to explore depth.

Prepare follow-ups connected to the role:

• Introduce a new constraint.
• Ask for a test that challenges the implementation.
• Review a questionable generated change.
• Explore concurrent requests or partial failures.
• Discuss release and monitoring requirements.

Keep the follow-up path comparable across candidates. If one person receives increasingly difficult questions while another stops after the happy path, the results become harder to interpret.

Completion speed is context. Its significance depends on the task, tool access, correctness, and the candidate’s understanding.

Should Any Part of the Interview Be AI-Free?

A short AI-free discussion can help verify understanding.

Ask the candidate to trace a failure, explain a decision, or predict how the implementation will behave. This gives you a direct opportunity to assess reasoning about their own work.

An AI-free coding section should serve a specific role requirement. Some engineers need to diagnose systems in restricted environments or continue working when an assistant is unavailable. If that matters for the job, assess it openly and disclose the format in advance.

Keep the section proportionate to what you need to learn.

Common Mistakes to Avoid

Scoring how much AI the candidate uses

Prompt count and generated lines reveal little by themselves.

One candidate may use AI for boilerplate and write critical logic manually. Another may generate a large patch and review it carefully. Assess the decisions and verified behavior.

Expecting constant narration

Continuous commentary can interfere with concentration.

Use checkpoints to explore meaningful choices, then give the candidate room to work.

Turning follow-ups into hidden traps

Candidates need a fair opportunity to clarify the task.

If a behavior is essential to completing the exercise, make that expectation available consistently. Follow-up questions should explore relevant reasoning.

Ignoring tool differences

Different models, account limits, and editor configurations can affect the session.

Standardize what you can and record exceptions. Account for those differences when discussing the result.

Treating a demo as complete verification

A successful demo establishes that the code handled the demonstrated case.

Use tests and discussion to explore other relevant conditions. Ask the candidate to identify what the demonstration leaves unproven.

Find Your Next Engineering Hire With Iterate

An interview gives you a way to evaluate candidates. A focused search brings the right people into that process.

Your sourcing strategy should reflect the same responsibilities as your assessment. When deciding where to recruit engineers, consider the experience, seniority, and technical ownership the role requires.

Iterate’s engineering recruiting team sources and screens candidates before introducing finalists to your team. Share the problems your next engineer will solve, the constraints they will face, and the capabilities you need to assess.

That brief gives the search a concrete target and helps your interviewers focus on relevant evidence.

Talk to Iterate about your next engineering hire.

Frequently Asked Questions

What is an AI-assisted coding interview?

An AI-assisted coding interview is a technical assessment that allows candidates to use AI tools while completing a programming task. Interviewers evaluate the implementation alongside the candidate’s problem framing, code comprehension, testing, debugging, and technical decisions.

Should candidates be allowed to use AI in coding interviews?

Allowing AI can be appropriate when engineers will use similar tools in the role. The assessment should establish whether candidates can evaluate generated work and take responsibility for the result. Explain the policy before the interview and provide comparable conditions.

How do you assess coding ability when a candidate uses AI?

Ask the candidate to explain critical code, predict its behavior, test failure cases, and respond to a relevant requirement change. These steps help establish whether they understand the implementation and can work beyond the assistant’s initial answer.

What makes a good AI-assisted coding interview task?

A good task is bounded, relevant to the role, and includes meaningful technical decisions. Extending an existing service, fixing a defect, or reviewing a flawed patch can reveal reasoning and verification skills within a manageable session.

How can you tell whether a candidate understands AI-generated code?

Ask them to trace the implementation with a specific input and predict what happens when a condition changes. Follow the explanation with a test or small modification where practical. Compare their prediction with the observed behavior.

Should every candidate use the same AI tool?

Providing the same tool and environment makes comparisons easier. If candidates use different setups, document the differences and avoid relying heavily on completion speed. Keep the task, core follow-up questions, and evaluation criteria consistent.

How long should an AI-assisted coding interview take?

A focused 60-minute session can include problem framing, implementation, verification, and review. The task should fit the allotted time while leaving room to discuss decisions and failure cases. More substantial assignments require a separate scope and clear expectations.

Should an AI-assisted coding interview include an AI-free section?

An AI-free discussion can help verify understanding of the candidate’s work. An AI-free coding section should address a specific job requirement and be disclosed in advance. Its purpose should be clear within the overall assessment.

Can an AI-assisted coding interview replace other technical interviews?

It provides evidence about practical problem-solving under defined conditions. Combine that evidence with relevant project discussions and other role-specific assessments when the job requires long-term ownership, collaboration, architecture, or operational responsibility.