AI Skills in Tech Interviews: What Interviewers Actually Look For (Interviewer's View)

I run technical interviews every week. About a year ago, I added a question to my repertoire that tells me more about candidates than most coding tasks: “How do you use AI in your daily work?”

The answers vary remarkably. Some developers wave it off. Others get carried away with enthusiasm. And a small group describes their AI workflow with a matter-of-factness that immediately signals: this person thinks critically, experiments, and can judge what works and what doesn’t.

That’s exactly the signal interviewers are looking for. And it’s exactly what most candidates never actively talk about. In this article, I’ll show you why AI skills have become the decisive differentiator in interviews, the three levels of AI competence I distinguish as an interviewer, and how to position yourself in the strongest one.

Why AI Skills Have Suddenly Become So Important in Interviews🔗

The New Reality in Tech Hiring🔗

AI has fundamentally changed how developers work. GitHub Copilot, ChatGPT, Claude, Cursor: if you don’t at least know these tools, you’ve spent the last year in a bubble. Companies know this. And they want to understand how their future employees handle this change.

This doesn’t just apply to startups or AI-focused companies. Traditional mid-sized software houses and enterprises are also finding that teams that productively use AI tools deliver faster. The question about AI skills is no longer a niche topic. It’s become a standard question, comparable to asking about version control ten years ago.

What the AI Question Really Tests🔗

When I ask candidates about AI, I’m not testing whether they can explain the Transformer architecture. I’m testing something much more fundamental: curiosity, adaptability, and judgment.

The AI question is essentially a proxy question. It shows me whether someone independently engages with new technologies, whether they’re willing to question existing workflows, and whether they can distinguish between hype and real value. These are qualities that go far beyond AI. A developer who uses AI tools reflectively won’t flounder when the next technological shift comes along.

Three Levels of AI Competence: Where Do You Stand?🔗

In my experience as an interviewer, the answers to the AI question fall into three clearly distinguishable levels. Each level sends a different signal to the interviewer.

Level 1: “I tried it once, it’s useless”🔗

You recognize these candidates by statements like: “I tested ChatGPT, but the code wasn’t good enough.” Or: “Copilot only suggests nonsense, I’d rather write it myself.”

What this answer signals is not technical superiority. It’s a lack of curiosity. Every new tool requires onboarding time. Someone who gives up after a single attempt shows the interviewer that they struggle to question established habits. And that’s a problem, because tools in software development change constantly.

There’s a difference between “I extensively tested AI tools and for my use case they weren’t helpful because…” and “I tried it once, it was nothing special.” The first sentence shows experience. The second shows closed-mindedness.

Level 2: “AI makes everything better!”🔗

The other extreme is candidates who present AI as the solution to every problem. They rave about productivity gains, talk about “10x developer” experiences, and when pressed, can neither name concrete limitations nor explain how they verify AI-generated code.

This profile worries me as an interviewer. Not because enthusiasm is bad, but because uncritical enthusiasm is a warning sign for production. A developer who pushes AI-generated code into the codebase without review is a security risk. Someone who can’t explain when AI hallucinates or why certain tasks aren’t suited for LLMs hasn’t truly understood the tool.

Level 2 is better than Level 1, but it’s not enough for a strong signal. What’s missing is the reflection.

Level 3: “I use AI daily, and here are the limits”🔗

This is the gold standard. Developers at Level 3 can tell a concrete story: which tool they use, for which tasks, and where they consciously choose not to use it. They describe not only what works, but also what they learned when it didn’t work.

An example from one of my recent interviews: a candidate described how she uses Claude for refactoring legacy code, but after several failures realized that AI becomes unreliable with highly domain-specific business logic. She adjusted her workflow accordingly, uses AI for boilerplate and tests, but still writes business logic herself. And she could explain why.

That’s the signal interviewers are looking for. It shows technical depth, independent thinking, and the ability to evaluate tools in context. These are exactly the qualities that separate a good candidate from an outstanding one.

What Interviewers Really Want to Hear🔗

Concrete Examples Beat Buzzwords🔗

“I use AI for more productivity” is an empty statement. “I use Copilot when writing unit tests for our Spring Boot services because it significantly speeds up the boilerplate for mocking setups. For more complex integration tests, I turn it off because the suggestions usually miss the context there” is an answer that convinces.

The difference is in the details. Name the specific tool. Describe the specific use case. And explain the specific decision you made. The STAR method (Situation, Task, Action, Result) works here too: What was the starting situation? What did you do? What was the result?

Knowing Limitations Shows Seniority🔗

When you talk about AI limitations in an interview, you demonstrate something many candidates underestimate: technical maturity. Anyone can claim AI is great. But only someone who has seriously engaged with it can explain why LLMs fail at certain tasks.

Good topics for the interview: hallucinations with niche frameworks or outdated APIs. Security risks from AI-generated code that doesn’t validate inputs. Privacy concerns when using cloud-based AI tools with proprietary code. Performance problems from AI-generated solutions that work but don’t scale.

Someone who can confidently address these topics signals to the interviewer: this person will also work responsibly with AI on the team and help others avoid common pitfalls.

In the technical interview, these questions come up increasingly often, even outside explicit AI rounds.

Your AI Workflow as a Conversation Starter🔗

An underestimated advantage of AI competence in interviews: it opens a dialogue. Instead of question-answer ping-pong, a real professional conversation emerges. You describe your workflow, the interviewer might share their experiences, and suddenly you’re no longer in an exam situation but in a conversation among colleagues.

You can use this effect deliberately. When the interviewer asks “How do you approach new features?”, you can naturally weave AI into your answer: “I start with a rough architecture sketch, then use Claude or ChatGPT to explore different implementation approaches, and then critically evaluate the suggestions against our existing architecture.”

That doesn’t sound like an AI lecture. It sounds like a thoughtful work process.

Practical Tips: How to Prepare🔗

Build Your AI Portfolio🔗

You don’t have to wait until you’re sitting in the interview to think about your AI experience. Start now by consciously documenting how you work with AI. Record which tools you use, for which tasks, and what you’ve learned along the way.

The goal is to have two to three concrete stories ready. Each story should answer: What was the problem? Which AI tool did I use? What worked, what didn’t? What did I learn from it?

This preparation doesn’t just help in the interview. It forces you to reflect on your own workflow, and that actually makes you a better developer.

The Right Formulations🔗

There are formulations that sound authentic, and those that sound memorized. The difference is often more subtle than you’d think.

[1] The more specific the tool and use case, the more credible the statement.
[2] A deliberate decision against a tool shows more competence than blindly using everything.

The pattern is clear: strong answers name specific tools, specific situations, and specific decisions. Weak answers stay abstract.

CodingCareer: Interview Preparation with an AI Focus🔗

The AI question in interviews isn’t an isolated topic. It’s part of a bigger picture: how do you present yourself as a developer who thinks critically, keeps growing, and takes responsibility for their own learning process?

At CodingCareer, we prepare developers specifically for exactly these interview situations. Our mock interviews simulate realistic scenarios where current topics like AI competence come up naturally. The difference from pure coding exercises: we train the entire conversation, not just the technical task.

Our coaches are developers themselves who regularly conduct interviews. They know which answers convince and which don’t, because they ask the questions themselves. In coaching sessions, you work on your individual story: how you talk about AI, how you talk about architecture decisions, and how you communicate your experience in a way that sticks in the conversation.

The coaching covers the entire application process. From CV optimization for German standards to HR interview preparation to salary negotiation. And with the pay-on-success model, you pay a reduced amount upfront and the rest only when you land the job.

Book your free 15-minute diagnostic session and find out how to not just pass the coding task in your next interview, but be remembered as the developer who truly understands how AI is changing the work.