I'm a senior in college preparing for new-grad software engineering applications, and it seems hiring processes are increasingly allowing or evaluating AI-assisted coding. I'm wondering how experienced engineers use these tools in their daily work. Is a reasonable workflow to use AI to clarify the problem, ask it to identify and explain bugs, research possible solutions, and then verify everything against the requirements and system design? What practical habits help you get the benefits of AI without losing your own debugging, coding, and design skills?
2 Answers
The workflow you described is basically the core engineering process: understand the problem, choose a solution, implement it, test it, and debug it. If AI handles every one of those steps without your review, you may produce code without developing the judgment needed to maintain it. Use it to explore alternatives, explain unfamiliar code, generate test cases, or point out edge cases, but make sure you can explain and validate every important decision yourself.
Treat AI like a very fast junior teammate, not an authority. Write down the requirements, constraints, and tests yourself first. Then ask the tool for a plan, review that plan, and have it make one small change at a time. Always inspect the diff, run the tests, and ask it to explain possible failure cases before accepting the result. For interviews and early-career growth, keep solving problems without AI regularly so you continue building independent debugging and design judgment.
This helps a lot—thanks. The idea of using small, reviewable changes and keeping up with unaided practice makes sense.
So would you recommend using AI selectively as a review and learning tool, rather than letting it drive the entire implementation?