How to Evaluate AI-Assisted Code in a Developer Interview
Every candidate now codes with an AI assistant. Here's how to interview for engineering judgement instead of typing speed, and the exact signals to score.
Table of contents
AI has changed how developers write code, but it has not replaced the need for good engineering judgement. Most candidates now use AI coding tools during development, so the best way to evaluate them is not by how quickly they produce code, but by how well they review and improve it. A strong developer can spot mistakes, explain trade-offs, justify design decisions, and identify issues an AI assistant overlooked. The goal of an interview is no longer to test typing speed. It is to assess the judgement needed to build reliable software.
In this article
- Why does the old coding interview no longer work?
- What should you test when every candidate codes with AI?
- What does a rigorous AI-assisted work evaluation look like?
- How do you score judgement instead of typing speed?
- How RocketDevs evaluates AI-assisted work, and why it holds
- Conclusion
- FAQ
Why does the old coding interview no longer work?
Traditional coding interviews were designed to measure whether a candidate could solve a self-contained programming problem. Today, that is one of the tasks AI coding assistants perform well. As a result, take-home assignments and timed algorithm challenges reveal far less about an engineer's ability than they once did.
The shift is reflected in industry data. Stack Overflow's 2025 Developer Survey found that 84% of developers already use or plan to use AI tools in their development workflow, up from 76% the previous year. In practice, employers should assume that most candidates have access to an AI coding assistant during the hiring process.
This also changes the value of live coding interviews. An analysis of 19,368 interviews conducted between July 2025 and January 2026 by Fabric found that 38.5% of candidates were flagged for AI-assisted cheating, rising to 48% for technical roles compared with 12% for sales positions. Coding exercises often have clear, verifiable answers, making them especially easy for AI tools to solve in real time. When an interview rewards producing the correct answer, it may be measuring how effectively a candidate uses AI rather than how well they think as an engineer.
What should you test when every candidate codes with AI?
When candidates use AI to write code, interviews should focus on the skills AI cannot replace. Test their judgement, code review ability, and whether they can explain and defend their technical decisions. AI can generate a first draft, but engineers are still responsible for validating, improving, and taking ownership of the final result.
Research shows that this review process has become a larger part of software development. Info-Tech Research Group's 2026 study of AI in software development, based on responses from 578 practitioners, found that 67% of developers believe AI-generated code requires more testing than code written by humans. The study also found that 84% of respondents use AI during the build phase, reinforcing that developers are spending more time reviewing and validating AI-generated code rather than writing everything from scratch.
Developers are also cautious about AI's reliability. Stack Overflow's 2025 Developer Survey found that 46% of developers actively distrust the accuracy of AI tools, while only 33% say they trust them. Strong candidates should be able to explain where AI-generated code might fail, identify potential risks, and justify the changes they would make before deploying it. Those are the skills that distinguish thoughtful engineers from candidates who simply accept AI-generated output without critical review. If you're hiring for roles where AI agents can touch production systems, the AI agent guardrails playbook covers the specific judgment calls those engineers need to make.
What does a rigorous AI-assisted work evaluation look like?
A strong AI-assisted coding interview focuses on how candidates review and improve AI-generated code rather than asking them to write everything from scratch. Instead of measuring how quickly they can produce a solution, assess how they identify flaws, explain design decisions, test assumptions, and defend their reasoning. The following interview exercises reveal those skills.
Start with a live "explain and critique your own code" exercise. Give the candidate a task and allow them to use an AI coding assistant. Once they have a solution, ask them to explain how it works, what assumptions it makes, where it could fail, and what they would change. Candidates who relied on AI without reviewing the output often struggle to answer these questions. Strong candidates can explain the design, discuss trade-offs, and identify weaknesses without prompting.
Next, evaluate how they think about testing. Passing tests do not always prove code is correct if the test suite has gaps. One of the most rigorous benchmarks for evaluating AI-generated software is SWE-bench Verified, a human-validated benchmark built with OpenAI using real GitHub issues and each repository's own test suite. Even so, a 2026 study introducing UTBoost found that after adding missing unit tests, 24.4% of patches previously marked as correct on the SWE-bench Verified leaderboard were actually incorrect because the original tests failed to detect the errors. This highlights an important interview question: not "Do the tests pass?" but "What could these tests be missing?" Ask candidates to identify an uncovered edge case, write an additional test, or explain how they would strengthen the test suite. These exercises demonstrate whether they engineer for correctness rather than relying on a passing test result.
Finally, evaluate how candidates review and improve their own work. In day-to-day software development, the strongest evidence of engineering judgement is often found in a pull request. Ask candidates to walk through a recent merged PR where an AI-generated solution was incomplete or incorrect. Have them explain what they changed, why they changed it, how reviewer feedback influenced the final implementation, and what they learned from the process. Unlike a take-home coding challenge, a pull request provides a clear record of technical decision-making and collaboration. For frontend hires specifically, our front-end developer interview questions and answers guide covers the domain-specific probes that pair well with this format.
The questions you ask matter just as much as the coding exercise itself. Strong engineers consistently demonstrate sound judgement, while weaker candidates tend to describe AI-generated output without critically evaluating it.
Signals that separate engineering judgement from prompt-tinkering
| What to ask | Strong answer (judgement) | Weak answer (prompt-tinkering) |
|---|---|---|
| Walk me through code your AI assistant just wrote. | Explains the intent, identifies assumptions, and points out areas they would review further. | Reads the code for the first time and describes what it does rather than why it was written that way. |
| Where could this fail in production? | Identifies specific edge cases, failure paths, or performance risks. | Says the code should work or only describes the ideal scenario. |
| What does this test suite not cover? | Identifies missing test cases and suggests additional tests to expose hidden bugs. | Treats passing tests as proof the implementation is correct. |
| The AI suggested this approach. Why did you choose it over the alternatives? | Compares trade-offs such as readability, performance, maintainability, and reliability before justifying the decision. | Accepts the AI's suggestion without considering other options. |
| Show me a pull request where the AI's first draft was wrong. | Explains what changed, why it changed, and how feedback improved the final solution. | Cannot provide an example or explain the changes that were made. |
| How do you decide when to trust AI-generated code? | Uses clear criteria based on testing, code review, risk, and the impact of failure. | Trusts the output as long as it compiles or passes the existing tests. |
How do you score judgement instead of typing speed?
The best way to evaluate AI-assisted coding is to score the skills that matter after the code has been generated. Let candidates use AI throughout the interview and assess how they review, explain, and improve the output. Banning AI creates an artificial test that no longer reflects how developers work. It can also disadvantage honest candidates while doing little to stop those determined to bypass the rules.
These criteria measure engineering judgement rather than coding speed. A candidate can score highly while using an AI assistant throughout the interview because the assessment focuses on the quality of their thinking, not how quickly they produce code.
This approach is especially useful for non-technical hiring managers. As discussed in RocketDevs' guide to evaluating developers when you are non-technical, you do not need to review every line of code to assess a candidate. Instead, evaluate how they reason about the solution, justify their decisions, and respond to feedback. The same principle applies to technical interview questions: the most valuable questions ask candidates to explain why they made a decision, what could go wrong, and how they would improve the solution, rather than simply asking them to solve a coding puzzle.
How RocketDevs evaluates AI-assisted work and why it holds
Thoroughly evaluating AI-assisted work takes time. Reviewing code, testing a candidate's reasoning, challenging their decisions, and assessing how they improve AI-generated output can take a technically skilled interviewer 6 to 8 hours per candidate. Most hiring teams cannot invest that much time, so they rely on coding exercises that are now easily completed with AI.
RocketDevs uses a more rigorous evaluation process. Every developer completes 6 to 8 hours of technical vetting focused on real-world engineering, code review, and decision-making rather than coding speed alone. That process resulted in only the top 2% of applicants passing the assessment in our 2025 cohort (2% pass rate, n=14,233), with more than 98% of applicants rejected. For a detailed breakdown of the process, see our guide on how to hire AI engineers.
If you need developers who can build reliable AI-assisted software, RocketDevs gives you access to engineers who have already been evaluated on the skills that matter most today: technical judgement, code review, and the ability to defend their work. Hiring starts at $9.99/hour for Associate developers, $21.99/hour for Mid-Senior developers, and $30.99/hour for Senior developers, with a 14-day risk-free trial. Our guide to hiring dedicated developers walks through each stage of the process in detail.
Conclusion
AI has changed software development, but it has not eliminated the need for skilled engineers. It has simply shifted the skills that matter most. The strongest developers are no longer the ones who can type the fastest or solve a coding puzzle under pressure. They are the ones who can review AI-generated code, identify hidden risks, strengthen weak solutions, and explain the reasoning behind every decision.
Hiring processes need to reflect that reality. Instead of testing whether candidates can produce code that an AI assistant can generate in seconds, focus on how they evaluate, improve, and defend that code. Judgement, critical thinking, and ownership are now stronger indicators of engineering ability than typing speed alone.
If your interview process still revolves around take-home assignments and algorithm challenges, it may be time to update it. By assessing how candidates work with AI rather than trying to prevent them from using it, you'll make better hiring decisions and build engineering teams that are prepared for the way software is developed today.
FAQ
Should I let candidates use AI during a coding interview? Yes. Most developers already use AI coding tools as part of their workflow. Stack Overflow's 2025 Developer Survey found that 84% of developers use or plan to use AI during development. Allowing candidates to use AI creates a more realistic interview and shifts the focus to the skills that matter most: reviewing code, making sound technical decisions, and defending those decisions under questioning.
How do I evaluate AI-assisted code without a take-home test? Give candidates a realistic coding task and allow them to use an AI assistant. Once they have a solution, ask them to explain how it works, identify its weaknesses, describe where it could fail, and improve the test suite. The quality of their review and reasoning provides a much stronger signal than a take-home assignment that AI can largely complete on its own.
What is the strongest sign that a developer understands AI-generated code? A strong candidate can clearly explain where they do not trust the AI-generated solution and why. They can identify potential bugs, edge cases, and design trade-offs before the code reaches production. Stack Overflow's 2025 Developer Survey found that 46% of developers distrust the accuracy of AI tools, compared with 33% who trust them. The best engineers treat AI-generated code as a starting point rather than a finished product.
Are algorithm and LeetCode-style interview questions still useful? They can still be useful for roles that require strong knowledge of algorithms and data structures, but they should not be the primary way to evaluate most software engineers. Modern AI tools perform well on many self-contained coding problems, making these exercises less effective at measuring real-world engineering ability. Questions that explore technical judgement, testing, trade-offs, and failure modes provide a much better indication of how a candidate will perform on the job.
Author
James Hitch, COO at RocketDevs. Last updated: 2026-07-29.
Sources
- Stack Overflow, 2025 Developer Survey (AI section)
- Fabric, State of AI Interview Cheating in 2026: Insights from 19,368 Interviews
- Info-Tech Research Group, AI in Software Development, 2026 (PR Newswire)
- SWE-bench Verified
- Yu, Zhu, He, Kang, "UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench" (arXiv:2506.09289)
Written by
James Hitch
COO
James Hitch is the COO of RocketDevs, where he runs sales, recruiting, and the vetting operation that accepts only the top 2–3% of developer applicants. He cares about putting accessible, elite engineering talent within reach of founders and startups worldwide, at a fair price. He writes about technical hiring, building AI-native engineering teams, and how startups can access elite developers affordably.
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