What AI Coding Tools Are Actually Changing About Technical Interviews

Technical interviews are shifting away from testing the ability to write code from scratch and toward evaluating how well candidates understand, debug, and optimize AI-generated code, according to a developer at RedYellow Technologies in Chennai. The developer notes that some companies now allow AI tools during interviews and assess how candidates direct and verify the tools. Key pitfalls include over-reliance on AI without understanding output and underrating communication and debugging skills.

A few years ago, a technical interview mostly tested one thing: can you write correct code, from memory, under pressure. That bar has quietly shifted — and a lot of developers preparing for interviews right now haven't fully clocked it. AI coding assistants are part of daily work at most companies now, from big IT services firms to small product teams. Interview panels have adjusted to that reality faster than most prep guides have. What's actually different now Interviewers care less about whether you can produce a function from scratch, and more about whether you understand what code is doing and why. It's increasingly common to be handed a piece of AI-generated code and asked to find the bug, justify a design decision, or optimize it — instead of writing something from zero on a whiteboard. Some companies go further and let you use AI tools during the technical round, then evaluate how well you direct the tool, verify its output, and catch its mistakes. The skill being tested has moved from "can you write code" to "can you reason clearly with code as your material." Three things I keep seeing candidates get wrong Treating a finished course or degree as the finish line. Completing a syllabus tells an employer you were exposed to concepts. It doesn't tell them you can apply those concepts to a messy, real-world problem — which is exactly what open-ended interview scenarios are designed to expose. Leaning on AI tools without understanding the output. Using an AI assistant while practicing at home is fine. The problem shows up when that habit surfaces in a live interview as an inability to explain your own solution. If you can't walk through why a piece of code works, a couple of follow-up questions will make that obvious fast. Underrating communication and debugging skills. As AI tools take on more initial code-writing, the human value shifts toward reviewing, debugging, and explaining decisions to teammates. Candidates who only practiced writing code — and never practiced talking through their reasoning out loud — often struggle here, even with solid technical knowledge. What's actually worth practicing If you're prepping for interviews right now, a few adjustments make a real difference: Fundamentals still matter — arguably more than before, because they're what separates someone who can direct and verify an AI tool from someone who's just along for the ride. I train aspiring developers at RedYellow Technologies https://www.redyellowtechnologies.com/ in Chennai, teaching Java, Python, AWS, DevOps, Data Science, and AI. Curious how this matches what others are seeing in interviews right now — drop a comment if your experience looks different.