After 30+ years coding, I’ve learned when AI assistants are confidently wrong – which happens more than you think. Here’s how to catch their mistakes and use them effectively.
The story
After 30+ years writing code and 8 years building AI solutions, I’ve developed a gut feeling for when an AI coding assistant is confidently wrong.
And that happens more than you’d think.
The numbers tell the story: 84% of developers now use AI assistants, but 46% don’t trust what they produce. That gap isn’t paranoia – it’s wisdom.
AI will generate code with flawless syntax that’s completely wrong for your business logic, your stack, or your constraints. The real skill isn’t using AI. It’s knowing when to override it.
Here’s what I’ve learned:
Break problems into small pieces. Instead of asking for a complete payment gateway, I’ll prompt for a single function to validate credit card numbers. Smaller tasks mean errors are easier to catch and the AI stays focused.
Guide relentlessly. I don’t ask once and move on. I rephrase, add context, state constraints explicitly. “Avoid deprecated libraries.” “Assume input is untrusted.” The more I steer, the less it hallucinates.
Trust the vibe. After decades of debugging, you develop a sense for when code looks right but isn’t. If something feels off – even when the AI sounds confident – I slow down and dig deeper.
Verify bold claims. AI loves suggesting outdated libraries, nonexistent APIs, or security anti-patterns with total confidence. I check every unfamiliar technique, especially in critical paths.
Keep humans in the loop. AI misses edge cases and tacit knowledge. Code reviews with teammates surface flaws neither I nor the machine would catch alone.
AI is a brainstorming partner, not a replacement for judgment. The futur
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