I trusted AI code too easily until production failures taught me otherwise. Here’s how to catch the confident mistakes before they bite you.
The story
I used to trust AI code suggestions way too easily. Big mistake.
The problem isn’t when AI fails obviously – it’s when it delivers code that *looks* perfect but contains hidden flaws you won’t catch until production. No hesitation in the output. No warning flags. Just smooth, authoritative garbage that will bite you later.
Here’s what actually works:
Review everything line by line. I don’t care how good the AI is – you’re responsible for what ships. Treat it like a junior dev who’s occasionally brilliant and occasionally confidently wrong.
Ask it to break itself. Before accepting any code, prompt: “What edge cases could break this?” Then write those tests yourself. The AI will happily generate test cases that expose its own logic holes.
Integrate in small chunks. Paste in large blocks and you’re asking for pain. Small increments, immediate testing, aggressive version control. When things go sideways – and they will – you want an easy rollback.
Be ruthlessly specific in prompts. “Write a sorting function” gets you mediocre results. “Write a Python quicksort that handles duplicates and maintains O[n log n]” gets you something actually useful. Vague prompts produce vague code.
Run linters on everything. ESLint, Pylint, SonarQube – whatever fits your stack. They catch patterns you’ll miss. Automate this through CI/CD so you can’t forget.
The developers who’ll thrive with AI aren’t generating the most code. They’re the ones who know when to trust suggestions and when to question them. That requires systematic verification, not blind acceptance.
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