AI Interview Questions for Software Engineers: Coding Assistants, Review and Judgment

Software engineering interviews now include questions about AI coding assistants that have nothing to do with machine learning: how you use them, how you review what they produce, what you never paste into one, how they change testing and documentation, and how you know when they are wrong. The interviewer is not looking for a position on AI; they are looking for an engineer who gets the productivity and keeps the judgment. This page gives twenty questions with answer outlines, organised by where AI touches the work, and a practice plan. The classic opener for engineers is in tell me about yourself for software engineers, and the generic version of the first question has a full structure in how do you use AI at work. This is not the ML or AI engineering interview, which is in AI engineer interview questions.

Writing code

1. How do you use AI coding assistants day to day? Specific tasks: boilerplate, tests, unfamiliar APIs, refactors; what you do not use them for; an example.
2. How do you review AI-generated code before committing it? Read every line as if a junior wrote it, run it, test the edges, check for invented APIs and licences, understand it well enough to maintain it.
3. Tell me about a time an assistant produced something wrong or dangerous. Specific: a plausible function that did not exist, a security hole, a subtle off-by-one; how you caught it; what you check now.
4. What do you never put into an AI tool? Secrets and keys, customer data, proprietary code outside the approved tool; the company's policy named.
5. Does AI make you faster, and where does it not? Honest: faster on the known and the boring, slower or neutral on novel design and debugging you do not understand.

Testing, debugging and quality

1. How do you use AI for testing? Generating cases and edge cases you then review; never trusting a test it wrote to prove the code it wrote.
2. How does AI change debugging for you? Explaining errors, suggesting hypotheses; you still reproduce and verify.
3. How do you keep code quality consistent when half the team uses assistants? The same review standard, linting and tests regardless of author; team norms written down.
4. How do you handle an assistant that confidently suggests an outdated pattern? Version awareness, documentation as source of truth, a habit of checking dates.
5. Tell me about a bug that AI helped you find, and one it caused. Both, honestly.

Design, documentation and judgment

1. Would you let AI design a system? It can propose; you decide, because it does not know your constraints, scale or history.
2. How do you use AI for documentation? Drafts from the code, edited for the reader; the architecture decisions written by you.
3. How do you evaluate a new AI tool for the team? A real task, a measure, security review, the policy; not adoption by enthusiasm.
4. How do you review a pull request you suspect was largely generated? The same way, plus a question to the author about why; understanding is the standard.
5. Where does your judgment stay essential? Requirements, trade-offs, security, what not to build.

Team, learning and the future

1. How do you help junior engineers use AI without stunting their learning? Use it after understanding, not instead; explain every line; pair.
2. How do you stay current without chasing every model? One or two sources, trying one thing at a time, a quarterly look.
3. What AI tools have you actually used in engineering work? Name them, what for, what you stopped using; no rankings.
4. How would you use AI differently in your first ninety days here? Learn the company's tools and policy, then your habits, then one team improvement with evidence.
5. Where is AI changing software engineering most, in your view? A short honest view with one example from your work.

A practice plan

• Write your one-paragraph position: AI for the known and the boring, reviewed like a junior's code, never fed secrets or data, judgment on design kept.
• Pick five of the twenty and answer them aloud with a real example each, including one where the assistant was wrong.
• Check whether the company has published anything about AI in its engineering practice; some have strong views.
• Run a mock interview from the posting and ask it to include AI questions; read the transcript for claims without a check in them.
• Prepare one question back about the team's tools, policy and review norms.

The broader engineering interview, including the coding and system-design rounds, has its own guide in AI interview help for software engineers.

Where CoPilot Interview fits

CoPilot Interview is an AI interview assistant whose free AI mock interview builds questions from your résumé and the job description, including the AI questions above if you ask for them, reads them aloud and scores each spoken answer out of ten with a transcript, which is where 'I review the output' turns into an answer that names what you check. The free plan gives 15 minutes a day of live help in sessions of up to 15 minutes, with AI mock interviews included. The product is built for practice; if you ever consider live help in a real interview, follow your employer's rules.

Disclosure: CoPilot Interview is our product, so weigh what we say about it accordingly.

Frequently asked questions

What AI questions are asked in software engineering interviews?

How you use coding assistants, how you review generated code, what you never paste into a tool, AI in testing and debugging, keeping quality consistent, design judgment, helping juniors learn, the tools you have used and your view of where it is going.

How should I answer 'how do you review AI-generated code'?

As if a junior wrote it: read every line, run it, test edges, check for invented APIs and licences, and understand it well enough to maintain it.

What should I say I never put into an AI tool?

Secrets and keys, customer data and proprietary code outside the approved tool. Name the policy you follow.

Is this the same as an AI engineer interview?

No. These are questions about using AI as a tool in ordinary engineering work; the ML and AI engineering interview is separate.