By the Innovyt Team · Last updated October 2026
When AI can write the code, a technical interview should test what AI can’t do on its own: understanding the problem, reviewing and fixing generated code, making technical decisions and explaining them. A balanced option is a hybrid process: one round of fundamentals without AI, one AI-assisted task, and one technical discussion. The goal is not to find developers who avoid AI, but engineers who use it well and know when not to trust it.
A candidate can now produce clean, test-passing code in minutes. That leaves employers with a new question: did they show engineering ability, or just the ability to prompt a tool? This guide covers whether to allow ChatGPT in interviews, what to measure, a 5-stage interview framework, and the questions to ask.
What to evaluate in an AI-era technical interview
| Skill | What to evaluate | Why AI doesn’t cover it |
| Problem understanding | Can they clarify requirements, limits and success criteria? | AI generates code; it doesn’t understand the actual problem |
| Code review | Can they find bugs, security issues and weak design in AI-generated code? | AI output still needs a human to verify it |
| Engineering judgment | Can they choose the right solution for performance, security, scale and maintainability? | Several solutions can work; picking the right one is a decision |
| Debugging | Can they work through broken or unfamiliar code? | Much real work is fixing existing systems |
| Communication | Can they explain their approach and trade-offs? | Explaining decisions shows they understand their own work |
Why traditional coding interviews no longer work on their own
Whiteboard coding, algorithm puzzles, timed tests and syntax questions measure some programming ability. But they were built for a time when writing code from memory was the hard part. Real developers search documentation, review existing code, use development tools, debug complex systems and work in teams. Their value is knowing whether a solution is correct, secure, scalable and actually solves the business problem.
What the data says about AI in development and hiring
- AI is now standard developer tooling. In Stack Overflow’s 2025 Developer Survey of more than 49,000 developers, 84% said they use or plan to use AI tools, and 51% of professional developers use them daily .
- But developers don’t fully trust it. In the same survey, 46% said they distrust the accuracy of AI output, while only 33% trust it. That gap is exactly why code review and verification skills matter in hiring.
- Candidates use AI too. In a Gartner survey of 3,290 job candidates, about 4 in 10 said they used AI during the application process.
- Interviewers are noticing. In an interviewing.io survey covered by The Pragmatic Engineer, 81% of interviewers at Big Tech companies said they had suspected candidates of using AI tools to cheat in interviews.
The takeaway: banning AI doesn’t remove it from the process, and ignoring it lets weaker candidates through. The interview has to be designed around it.
How interviewers are adapting
Interviewers surveyed by The Pragmatic Engineer described several practical changes:
- Using more complex problems, where AI-generated answers tend to show tell-tale patterns
- Asking candidates to extend existing logic instead of writing a fresh solution
- Rewording standard questions so they don’t match well-known problem sets
- Putting more weight on problem-solving and less on producing code
AI dependency vs AI fluency: what employers should test
The real line is not “uses AI” vs “doesn’t use AI.” It is dependency vs fluency.
| AI-dependent candidate | AI-fluent developer |
| Copies AI-generated solutions | Uses AI as a productivity tool |
| Can’t explain the logic | Reviews and questions the output |
| Struggles when requirements change | Improves and adapts the solution |
| Trusts the tool by default | Applies engineering judgment, knows when to reject a suggestion |
Two candidates can submit almost identical code. One understood the problem, used AI carefully, reviewed the output and can explain every decision. The other copied the output and can’t modify it. The code looks the same; the engineering ability doesn’t. That is why the interview has to evaluate the process, not just the final code.
Should you allow ChatGPT in coding interviews?
There is no single right answer. It depends on the role, the experience level, how your team actually works, and which skills you need to measure.
| Approach | How it works | Best for |
| AI restricted | Some or all sections done without AI | Programming fundamentals, independent reasoning, entry-level roles |
| AI allowed | Candidates use tools like ChatGPT and explain their process | Real-world engineering roles, senior developers, AI-enabled teams |
| Hybrid | Round 1: fundamentals without AI. Round 2: AI-assisted task. Round 3: technical discussion and review | A broad view of ability across levels |
Whatever you choose, write an AI usage policy before interviews start and share it with candidates: whether AI is allowed, which tools, how usage should be disclosed, and which skills each round evaluates.
Match the interview to the level
- Junior developers: programming fundamentals, debugging ability, learning capability
- Senior developers: architecture decisions, system design, technical leadership, code quality
One interview format can’t accurately evaluate every engineering level.
A 5-stage AI technical interview framework
A modern interview can follow the way developers actually work: Understand → Create → Review → Improve → Explain. Each stage tests a different skill.
- Understand the problem. Before touching any AI tool, the candidate asks relevant questions and identifies requirements, limits and success criteria. A developer who asks AI for code before understanding the problem creates risk.
- Create a solution with available tools. AI can be part of this stage. Evaluate prompt quality, tool choice, technical reasoning and how well they guide the AI’s output.
- Review the solution. Give them AI-generated code, their own solution or existing application code. Can they spot logic errors, security concerns, scalability issues and missing tests?
- Improve it. Ask them to refactor inefficient code, improve performance, fix security issues or simplify the architecture. AI produces a first draft; strong engineers make it better.
- Explain their decisions. Why this solution, how AI helped, where AI failed, and which trade-offs they weighed. This is what separates engineers from people who only operate tools.
Four assessment formats that work with AI
| Format | What the candidate does | What it reveals |
| AI-assisted coding challenge | “Use any available tools to build this feature and explain your process.” | How they interact with AI, whether they verify and improve its output |
| AI code review exercise | Reviews AI-generated code before it ships | Logic errors, security risks, performance problems, missing requirements they catch |
| Real-world project simulation | Builds an API feature, debugs an app issue, improves database performance or designs a scalable service | Practical ability beyond isolated puzzles |
| Debugging task | Gets a broken application, buggy AI code or a performance issue: “How would you find and fix this?” | Reasoning, debugging approach, engineering judgment |
Follow every format with a short technical discussion: why they chose that approach, what alternatives exist, its limitations, and what they’d improve next. This confirms they understand their own work.
Test business understanding too. AI can produce technically correct code that misses the real requirement. Ask a candidate to “build a notification system” and a strong engineer will ask about the number of users, delivery reliability, failure handling and system requirements before writing anything.
5 interview questions for the AI era
| Question | What it tests |
| “An AI tool generated this code. What problems do you see?” | Code review, technical knowledge, attention to detail |
| “How would you verify AI-generated code is safe to use in production?” | Security awareness, engineering maturity |
| “Describe a situation where you would not trust AI-generated code.” | Critical thinking, understanding of AI limits |
| “If AI writes 80% of the code, what value does an experienced engineer add?” | Engineering mindset, decision-making |
| “How would you improve a solution created by an AI assistant?” | Problem-solving, optimization |
These questions reward the skills that matter more than raw coding speed: critical thinking, technical communication, engineering judgment, breaking problems into parts, quality awareness and adaptability.
4 mistakes companies make in AI-era technical interviews
- Banning AI completely. Developers already use documentation, search and AI assistants every day. Removing all of them can measure an artificial limitation instead of real capability.
- Allowing AI without evaluating how it’s used. Allowing AI doesn’t mean accepting any generated answer. The question isn’t “Did AI write the code?” but “Did the candidate understand and improve it?”
- Relying only on algorithm challenges. They still have a place, but they miss debugging, communication, architecture thinking and collaboration.
- Ignoring AI skills altogether. Writing effective prompts, validating AI responses, fitting AI into workflows and knowing its limits are now part of the job.
How Innovyt helps companies hire AI-ready developers
Innovyt is a US-based staffing company providing permanent, contract, contract-to-hire and offshore IT staffing. Innovyt’s recruiters source and screen technical candidates against each client’s business needs, using an in-house talent database and proprietary assessment tools, and every client works with a dedicated account manager.
Hiring through a staffing partner? Ask how they test developers
If a staffing partner screens developers before you see them, their screening process becomes part of your interview. Ask them:
- Is there a live technical round, or only take-home tests?
- Are candidates allowed to use AI, and how is that evaluated?
- Do candidates review or debug code, not just write it?
- Do they explain their decisions to an interviewer?
- How is the screening adjusted for junior vs senior roles?
If you’re hiring through Innovyt, share your AI usage policy and the skills that matter for the role with your account manager so candidate screening matches your interview.
Frequently asked questions
What is an AI technical interview? A developer assessment that evaluates programming ability, technical judgment and problem-solving while accounting for how candidates use AI tools.
Should companies allow ChatGPT during coding interviews? It depends on the role. Restrict AI to test fundamentals, allow it to see real-world working style, or use a hybrid of both. In every case, evaluate whether the candidate uses AI responsibly and can explain the result.
How should employers test developers when AI can write code? Focus on problem understanding, code review, debugging, system design, communication and AI collaboration skills, and evaluate the process, not just the final code.
What is the difference between AI dependency and AI fluency? An AI-dependent candidate copies output and can’t explain or change it. An AI-fluent developer uses AI for speed, reviews its output, improves it and knows when to reject it.
Can AI replace software engineers? AI improves developer productivity, but engineers are still needed for problem-solving, architecture decisions, security and understanding the business.
Build a developer hiring process for the AI era
Developers already use AI. The real challenge is evaluating engineering ability when code can be generated instantly. The strongest hires won’t compete with AI; they’ll combine it with sound engineering judgment. If you’re hiring developers and want candidates screened for skills, judgment and AI fluency, talk to Innovyt.
Talk to Innovyt’s IT staffing team →