AI-Assisted Coding Interviews Should Test the Review Loop

An AI-assisted coding interview shouldn’t be a typing contest with a chatbot bolted on. The useful question is whether a candidate can frame the task, use tools deliberately, verify results, and explain trade-offs. That is a stronger signal of engineering judgment than a polished first draft alone—but only if the exercise resembles the job and is scored consistently.

The debate is real; the evidence is still anecdotal

In a recent Ask HN thread, one hiring manager described a 20–25-minute exercise: review a deliberately flawed, small REST API without AI and explain some of the defects. Other participants described AI-permitted tasks followed by a change request. This is evidence of live experimentation, not proof that any one format predicts job performance. S1

A Y Combinator profile of Adam founding engineer Dylan Anderson offers a narrow role-level example: he completed a work trial and lists Claude Code and Codex as AI coding tools. That doesn’t validate work trials as a hiring method, but it does underline why teams should design assessments around actual responsibilities rather than a fashionable AI policy. S3

HackerRank, an assessment vendor, recommends a Plan/Build/Review sequence: surface the candidate’s approach, let them build with AI available, then probe their reasoning. Its suggested exercises include debugging and introducing a requirement change mid-task. Treat this as vendor guidance, not independent proof of effectiveness. S2

Make the interview job-like and legible

Publish the tool rules and environment in advance. If the role expects AI assistance, let candidates use it; if unaided fundamentals matter, assess that separately and say so. Don’t advertise an AI-allowed exercise and then penalize a candidate for using the tools you permitted.

Give a bounded task—such as extending a small service—and ask the candidate to state the goal, constraints, and plan before they start prompting. Midway through, change one requirement. Observe whether they clarify ambiguity, test edge cases, challenge a plausible but unsuitable suggestion, and adapt the implementation. Finish by asking them to explain a design choice and how they verified the result.

Score decisions, not prompt theater

Use the same short rubric across interviewers: problem framing, verification, adaptation, communication, and implementation quality. Don’t award points for typing speed, prompt length, or familiarity with a particular brand of assistant. A clean result matters, but so does whether the candidate can explain what they delegated, what they checked, and what they changed.

If independent coding is genuinely part of the job, a brief, clearly labeled no-AI segment can test it. Otherwise, a blanket ban may measure a narrower workflow than the role requires. The goal isn’t to hire the best prompter; it’s to find someone who can steer tools, catch their limits, and own the code that ships.