The AI and machine learning industry is evolving rapidly. With AI coding agents such as Devin and Cursor, and advanced LLMs capable of generating scripts in seconds, many of my mentees frequently ask: Should we still practice coding for AI/ML interviews?
This is a valid question. If AI can instantly generate a PyTorch training loop or a complex Pandas data pipeline, why invest hours practicing coding problems or writing Python from scratch?
Relying solely on AI to write code without understanding the fundamentals is a significant mistake in your AI/ML journey. While AI is transforming development, the core qualities of a great engineer remain unchanged.
Here is why coding fundamentals remain your greatest advantage in interviews and in practice.
So, Should You Still Practice Coding for AI/ML Interviews?
AI coding tools now go far beyond autocomplete. Agents can read repositories, plan changes, write code, run tests, and iterate on failures. They save significant time on boilerplate, data cleaning scripts, and quick prototypes.
However, writing code is only part of the job. The role also involves deciding what to build, verifying functionality, and resolving issues. While AI accelerates initial development, you are still responsible for validation and debugging, which require strong code comprehension and reasoning.
What Recent Research Says
Three recent studies are particularly relevant.
Experienced developers were slower with AI, yet felt faster. In a randomized controlled trial by METR, 16 experienced open-source developers completed 246 real tasks in their own repositories. They expected AI to make them about 24% faster. Instead, tasks took 19% longer when they used AI, because they spent more time prompting, waiting, and reviewing output. METR notes the study covered early-2025 tools and a small group, so it isn’t a verdict on every tool today. The gap between how fast people felt and how fast they were is the part to remember.
Relying on AI can weaken the skills you need to check it. In January 2026, Anthropic published a randomized trial where developers learned an unfamiliar Python library, with or without AI help. On a follow-up quiz without AI, the AI group scored 50% compared to 67% for the hand-coding group, and the biggest gap was on debugging questions. Participants who asked for explanations while using AI did much better than those who delegated the work.
AI-generated code still needs a careful reviewer. Veracode’s 2026 GenAI Code Security Report found the average security pass rate across tested models sits at 56%, barely changed from the year before. Code can run fine and still ship with security flaws.
The pattern is clear! AI is valuable, but you must understand code well enough to identify and correct its errors.
What Interviewers Are Actually Testing
AI/ML interviews rarely reward memorized syntax. Instead, they assess your ability to analyze problems using data and articulate your reasoning:
- Can you break down a problem? Translating a vague question into clear steps is a skill that cannot be replaced by prompts.
- Can you read code and identify bugs? Issues such as incorrect join keys, silent type changes, and data leakage are common in real ML work.
- Can you validate an answer? If you cannot verify a result manually on a small example, you should not trust it on a larger scale.
- Can you work without a safety net? Many interviews are conducted live, without access to AI tools.
Every item here is a skill the Anthropic study found to be at risk when people over-delegate.
A Resource That Helps
If you are seeking structured practice on the skills interviewers test—such as problem decomposition, careful code reading, and debugging without assistance—I wrote Cracking your First AI/ML Interview to address these needs.
How to Practice Coding in the AI Era
You do not need to avoid AI. Instead, use it in ways that continue to develop your own skills.
- Attempt the problem independently before seeking assistance. Spend 15 to 20 minutes working through it; meaningful learning occurs during this process.
- Use AI as a reviewer and explainer. Ask why a particular line works or which edge cases you may have missed. This comprehension-first approach is associated with better outcomes in research.
- Always verify results. Test AI-generated code on small inputs where you can confirm the expected outcome.
- Practice intentional debugging. Deliberately break working code and then fix it. Repeated exposure builds confidence in interpreting error messages.
Test Your Skills with Five Hands-On Problems
For a practical starting point, I curated 5 Python and Pandas Problems to Practice Before an AI/ML Interview. These hands-on problems reflect the data manipulation questions commonly encountered.
Attempt each problem independently before reviewing the solutions. This approach quickly reveals which fundamentals are strong and which require further development.
The Takeaway
AI coding agents are here to stay, and it is important to use them effectively. As AI generates more code, the ability to read, test, and fix that code becomes increasingly valuable. Interviews are designed to identify candidates with these skills.
Continue practicing, and treat AI as a valuable collaborator rather than a substitute for your own judgment. Successful developers neither avoid AI nor rely on it blindly. They understand code well enough to trust it when it is correct and identify errors when it is not.
Thank you for reading this article on the importance of practicing coding for AI/ML interviews. For further insights on AI and machine learning, please follow me on Instagram.
