How to Assess AI-Assisted Coding in a Developer Interview
An AI-assisted coding interview should assess two things together: whether the candidate produces correct, maintainable software and whether they can explain, verify, and improve the work. Allowing an assistant changes how candidates work; it does not remove the need to judge the result.
This guide provides a 60-minute interview plan, a coupon-service exercise, acceptance criteria, and an illustrative scorecard. Adapt it to the role and use consistent conditions across candidates. For the broader interview process, see our remote developer interview guide.
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Why AI-assisted coding interviews change what you test
A blank-editor test no longer shows how a developer really works, because many developers now build with an assistant open next to the editor. An AI-assisted interview tests the skill you pay for: turning a vague task into correct, maintainable code with a fast tool that is sometimes wrong. Choose a task that resembles the work the person would actually own.
What the format looks like
In a typical session, the candidate gets a small multi-file codebase, an AI assistant, and 45 to 60 minutes. The task moves in phases: fix a bug, add a feature, then handle a harder requirement such as a new edge case or more load. Prompt visibility depends on the agreed tools and screen-sharing setup. Confirm what can be observed before the session, and assess the resulting code alongside the decisions that produced it.
Two setups are common, and the choice changes what you can observe.
| Structured | Open-ended | |
|---|---|---|
| Environment | Shared interview environment with an approved assistant | Candidate's own editor and AI tools over screen share |
| Task | Fixed problem with set phases | Extend an existing repo or build a small feature |
| Strength | Consistent starting conditions; verify available prompt visibility | Shows real habits and tool fluency |
| Risk | Unfamiliar tools can slow a strong candidate | Harder to compare, more setup time |
Use the structured format for first-round screening across many candidates. Save the open-ended format for a final round with a senior hire, where working habits matter more than a comparable score.
Why a passing solution tells you less now
Classic algorithm puzzles can be solved with a single prompt, so a working answer proves little. A candidate can paste a solution, watch the tests go green, and still be unable to say why it works or when it breaks. What you are really buying is the second step: the developer who reads the draft, spots the wrong assumption, and fixes it before it reaches production.
AI also fails in ways that a weaker reviewer will miss. It invents helper functions that do not exist in your repo, handles the happy path and skips the edge case, and follows a pattern that does not match the rest of the codebase. Code that looks right is the hardest kind to review.
Passing tests provide evidence against the cases covered. Review the implementation, test quality, and explanation before drawing a conclusion.
How to run the interview step by step
Run it as a 60-minute session with a shared screen, a codebase the candidate has never seen, and three phases of increasing difficulty. Keep the task small enough to finish and ambiguous enough to require judgment. Done this way, an AI-assisted coding interview produces evidence you can score instead of an impression.
Prepare the codebase and the candidate
Build the exercise yourself, in the stack of the role. Aim for four to six files and a few hundred lines, with a test suite, one seeded bug, and one missing feature. Do not use your production code. Candidates should not paste proprietary code into third-party tools, and you should not ask them to.
Tell the candidate the format before the day. Say which tools are allowed, whether they can bring their own, what prompts and tool activity will be visible, and what you will score. Provide a short tool warm-up and agreed checkpoints for explanations. Candidates may work quietly between checkpoints; assess understanding, not constant narration, accent, or speed of speech. Offer appropriate accommodations consistently.
Check these items before every session:
- A repo with passing and failing tests, one seeded bug, and one missing feature
- A written scorecard shared by everyone who interviews
- Clear rules on tools, screen sharing, and recording consent
- A fallback plan if the assistant or platform goes down mid-session
Use a timed, phased task
Split the hour into phases so each one isolates a skill. Here is an example: a small order service where a discount code is applied twice when the cart updates (the seeded bug). Phase two adds a rule that limits each code to one use per customer. Phase three changes the requirement so codes now expire.
| Time | Phase | Candidate does | You learn |
|---|---|---|---|
| 0 to 10 min | Orient and plan | Reads code, asks questions, states a plan, AI off | Decomposition, requirement questions |
| 10 to 25 min | Fix the seeded bug | Uses AI as they choose, runs tests | Debugging, verification |
| 25 to 45 min | Add the feature | Prompts, reviews, edits the output | Prompt quality, control of design |
| 45 to 55 min | Handle the twist | Adapts to your changed requirement | Flexibility, reasoning |
| 55 to 60 min | Walk through | Explains the final diff | Ownership of the code |
Keeping the assistant off for the first ten minutes is deliberate. It shows whether the candidate can read code and form a plan without a tool, and it stops the habit of pasting the whole problem into a chat window. Use the final walkthrough to check understanding and remaining limitations alongside the code and tests.
Give each phase one job, and you can score each skill separately instead of guessing from a single result.
Copyable exercise brief: coupon service
Give the candidate a small prepared repository containing an order service, an in-memory redemption store, and tests. Use synthetic customers and no payment integration. The service calculates totals in integer cents; there is one currency, one coupon per order, and no taxes or shipping. Supply a controllable UTC clock. This keeps the exercise focused on business rules rather than environment setup.
- Seeded bug: refreshing a cart applies a 10% coupon to its already-discounted total. A 10,000-cent subtotal becomes 9,000, then incorrectly becomes 8,100 on refresh.
- Fix: recalculate from the undiscounted line-item subtotal. Repeated refreshes must leave the same total when quantities do not change.
- Feature: allow a customer to redeem a coupon only once across completed orders. Cart previews must not consume the coupon. Completing the same order twice must not create a second redemption.
- Twist: a coupon is invalid at or after its UTC expiry instant. Check at order completion, even if the earlier cart preview was valid.
- Input rules: accept only nonnegative integer prices and positive integer quantities. Reject an empty order. Normalize coupon codes by trimming whitespace and using uppercase. Round the discount down to whole cents.
| Check | Expected behavior |
|---|---|
| Preview 10,000 cents with 10% off, twice | 9,000 cents both times; no redemption recorded. |
| Change subtotal to 20,000 cents | 18,000 cents, recalculated from original prices. |
| Complete order A, then repeat completion of A | Return the same outcome; one redemption only. |
| Same customer completes order B with that code | Reject the second redemption; another customer can use the code. |
| Completion immediately before, at, and after expiry | Allow before expiry; reject at and after expiry. |
| Empty order, negative price, or invalid quantity | Reject with a documented validation error. |
Assessor notes: prepare the starter repository and verify its setup before interviewing. Seed only the repeated-discount bug; leave redemption and expiry as clearly marked missing features. Check that the fix does not mutate original prices, that tests control time, and that retries are distinguished from separate orders. For a senior role, ask how a database transaction or uniqueness constraint would prevent two simultaneous redemptions. Discuss that design rather than requiring production concurrency infrastructure in an hour. Evaluate incomplete work against observed evidence and the agreed scope, not an assumption that every candidate must finish every extension.
The skills to score: prompting, verifying, debugging
Assess planning, tool use, verification, debugging, and explanation alongside code correctness. Record observations rather than inferring skill from presentation style.
Planning and prompting
Strong candidates read before they prompt. They ask one or two clarifying questions, name the files they expect to change, and say what they will do before they do it. When they do prompt, they supply context, constraints, and the pattern to follow. Weak candidates paste the raw task into the chat and build on whatever returns.
Here is an example of the difference, using the order service:
Weak:
Add a coupon limit to the order service.
Strong:
In orders/service.py, apply_coupon() allows unlimited use of a code.
Add a check so one code can be used once per customer_id. Reuse the
CouponError pattern already in this file. Do not change the function
signature. Show only the changed function.
Do not score clever wording. Score whether the prompt contains what a colleague would need to do the job: where, what, which constraints, and which existing pattern. Prompt quality is just communication, and the AI is a very fast colleague with no context.
Verifying output
Verification is the highest-signal behavior in the session. Strong candidates run the tests after each generated change, read the diff before accepting it, and name at least one assumption the assistant made. They write a quick test for an edge case the prompt never mentioned, such as an empty cart or a code in a different letter case.
Weak candidates glance at the output, say it looks right, and move on. A typical failure is the assistant calling a helper function that does not exist in the repo, and the candidate only learning that when the run crashes with ten minutes left. Reading the diff and running focused checks can reveal the problem earlier.
Debugging and explaining
When generated code fails, watch what happens next. A strong candidate forms a hypothesis, reproduces the failure, narrows it to one function, and then decides whether to re-prompt or fix it by hand. A weak candidate pastes the error back into the chat, gets a different error, and repeats until the code no longer resembles anything they understand.
Then test explanation. Ask them to walk through any generated block and say what changes if the input is null or the list is empty. Allow time to inspect the code and explain. Follow up on specific uncertainty rather than treating a pause as proof of weak understanding.
The loop that sinks candidates is prompt, error, prompt again, with no hypothesis anywhere in between.
A scorecard and questions you can reuse
Use one scorecard for every candidate and fill it in during the session, not afterward. A shared rubric can reduce inconsistency, but interviewers should calibrate using sample submissions and discuss differences in their evidence.
The scorecard
The following weights are a suggested starting point, not validated predictors of job performance. Calibrate them to the role before interviewing. Rate each category from 1 to 5 and multiply by its decimal weight to obtain a weighted average out of 5.
| Category | Weight | Weak evidence | Strong evidence |
|---|---|---|---|
| Correctness and maintainability | 30% | Core requirements fail or changes introduce obvious regressions. | Acceptance tests pass, design fits the codebase, and limitations are documented. |
| Verification and security | 25% | Accepts generated output without review or meaningful checks. | Reviews the diff, adds boundary tests, and identifies unsafe assumptions. |
| Debugging | 20% | Changes code repeatedly without investigating the failure. | Reproduces the issue, tests a hypothesis, and validates the fix. |
| Planning and tool use | 15% | Unclear scope and uncontrolled changes. | Clarifies requirements, limits scope, and gives useful context when using AI. |
| Explanation and handoff | 10% | Cannot explain important behavior after follow-up. | Explains decisions, remaining risks, and next steps clearly at agreed checkpoints. |
A score of 3 should mean the candidate meets your documented role expectations. Record concrete examples for every score. For example, scores of 4, 3, 4, 3, and 4 yield 3.60 out of 5. Do not use that number as an automatic hiring cutoff. Investigate serious correctness or security gaps and distinguish missing evidence, time constraints, and actual misunderstanding.
Questions that reveal judgment
Good AI-assisted coding interview questions are short follow-ups asked at specific moments. Pair each with what a strong answer sounds like so interviewers score the same way.
| Ask this | A strong answer sounds like |
|---|---|
| Before the chat opens: what will you change first? | Names specific files and an order, with a reason |
| After a generated change: what did the assistant assume? | Points to a concrete assumption, such as case sensitivity or null input |
| Why did you reject that suggestion? | Ties it to the requirement or the existing architecture |
| How would you know this is correct? | Lists tests, edge cases, and a manual check |
| The assistant keeps giving the same wrong answer. Now what? | Stops prompting, reproduces the bug, reads the code, may write it by hand |
| When would you not use AI here? | Short, familiar edits, security-sensitive code, or areas they cannot verify |
Listen for specifics. A vague answer needs follow-up. A concrete answer might be: "It assumes previewing a cart consumes the coupon; I will add a test showing previews do not record redemptions." Score the subsequent reasoning and work against the rubric.
Mistakes to avoid and how to adapt by role
Two important interviewer errors are judging output without examining reasoning and using inconsistent conditions across candidates. Fix those two and the rest of the process gets easier. The role you hire for then decides which seeded problems are worth including.
Mistakes interviewers make
Avoid these, because each one corrupts the comparison between candidates:
- Penalizing tool use. If you allow AI, a candidate who uses it well should not lose points for using it.
- Rewarding speed. The fastest candidate may be the one who skipped review. Score the reasoning, not lines of code.
- Changing the rules between candidates. Different tools, time limits, or hints can make scores harder to compare; document agreed accommodations and any interruptions.
- Skipping an unassisted check. An initial unassisted code review gives additional evidence of independent understanding.
- Ignoring tool unfamiliarity. Allow a short warm-up so a candidate is not marked down for finding the chat panel.
Adapt the task to the role
Seed a problem that the role's daily work would expose. For a front-end developer, plant an accessibility gap in generated markup. For a .NET developer, use a query that loads related records one at a time. For a Salesforce role, check whether generated Apex runs queries inside a loop, which hits governor limits. For WordPress and WooCommerce work, distinguish input validation and sanitization from SQL parameterization and contextual output escaping. For custom SQL, use the appropriate wpdb::prepare placeholders; sanitization alone does not prevent SQL injection. If you want a broader hire, a full-stack exercise can combine a backend bug with a UI change.
| Role | Seeded issue | What verification should catch |
|---|---|---|
| Front-end | Generated component lacks labels and keyboard support | Accessibility and state bugs |
| .NET | Query runs once per record | Performance and null handling |
| Salesforce | Query inside a loop | Governor limit failures |
| WordPress or WooCommerce | User input concatenated into SQL or output without contextual escaping | Prepared queries, input checks, and appropriate output escaping |
Hiring dedicated developers in India
GlobalEmployees sources AI tool and workflow experience when you request it, and assesses that experience role by role. We do not claim that every candidate holds an AI certification, so your interview is where you confirm the skill. Bring your scorecard to the shortlist stage when you hire employees in India, run the session on a video call, and assess how clearly the candidate explains decisions and records a useful handoff. Written communication also matters when working hours differ.
Test the behavior you will depend on every day: a developer who checks the tool's work before you have to.
Assess the result and the reasoning together
Use a small realistic exercise, clear tool rules, and consistent evaluation criteria. Inspect the code, run the tests, and ask the candidate to explain important decisions. The strongest evidence combines working software with an ability to identify its limitations and improve it safely.
If you are hiring developers in India and want candidates whose AI workflow you can verify in your own interview, share your role brief with GlobalEmployees. We will source against your requirements, you will interview and select, and you will manage the work from day one.