When AI Codes For You: What You Still Need to Know

Your IDE suggests a function. You accept it. It works. You move on. A week later, that code causes a production bug. The AI wrote it. You shipped it. Who's responsible?

You are.

AI coding assistants are powerful. They're also confidently wrong. They hallucinate APIs. They copy patterns from bad examples. They optimize for "looks right" over "is right." Your job when AI codes for you isn't to blindly accept—it's to understand, verify, and own.

Treat AI output as a first draft

Never paste and ship. Treat every AI-generated snippet as a draft that needs your review. Ask:

• Does this solve the right problem? AI often solves a problem, not your problem. It might implement a generic solution when you need something specific.
• Do I understand every line? If you can't explain it, don't ship it. You'll be the one debugging at 2 AM.
• What edge cases did it miss? AI tends to handle the happy path. Empty inputs, null values, race conditions—you need to think about those.

I've seen engineers accept AI-generated code that looked perfect but failed on empty arrays, or assumed a specific date format, or had a subtle off-by-one error. The AI didn't know your domain. You do.

The fundamentals still matter

AI can write a function. It can't tell you when to use that function. It can't tell you why this approach is better than that one. It can't design your system.

Interviewers know this. They're not testing whether you can prompt an AI. They're testing whether you understand complexity, data structures, trade-offs. They're testing whether you can reason about code when the AI gets it wrong.

The engineers who thrive with AI are the ones who already had strong fundamentals. They use AI to go faster, not to hide gaps. If you're relying on AI because you don't understand the basics, you're building on sand.

Use AI to learn, not to skip

When AI generates code you don't understand, that's a learning opportunity. Don't just accept it—ask: "Why did it do it this way? What's the time complexity? What would break if I changed this?"

Use AI like a tutor, not a shortcut. Ask it to explain. Ask for alternatives. Ask what the trade-offs are. Then verify. Check the documentation. Write a test. The goal isn't to get code faster—it's to understand the code you get.

What to verify before you ship

Correctness: Does it actually work? Write a test. Try edge cases. Don't assume.

Security: AI loves to suggest eval(), string concatenation for SQL, and other patterns that create vulnerabilities. Review with security in mind.

Performance: AI often picks the simplest solution. Is it O(n²) when O(n) would work? Does it load everything into memory when streaming would be better?

Maintainability: Is this code you'd want to debug in six months? Is it readable? Does it follow your team's patterns?

The interview reality

Interviews don't let you use AI. You're on a whiteboard or a shared editor. You have to think, reason, and write code yourself.

The engineers who lean too heavily on AI struggle in interviews. They've forgotten how to reason through a problem from scratch. They've forgotten the syntax. They've forgotten the patterns.

Use AI to accelerate, but practice without it. Do a LeetCode problem without Copilot. Design a system without ChatGPT. Your interview performance depends on what's in your head, not in your tabs.

Own the code you ship

When you accept AI-generated code, it becomes yours. You're responsible for what it does. You're responsible when it breaks. You're responsible for understanding it.

The best engineers I know use AI heavily—and they review everything. They understand the code they ship. They know when to push back. They know when the AI is wrong.

Use AI. But don't outsource your judgment.