Your AI coding assistant’s confident answers might be dead wrong. After 30 years of coding, here’s how to spot the red flags before your code breaks in production.
The story
Ever wonder why your AI coding assistant seems so sure about code that breaks everything in production?
After three decades writing software, I’ve realized the hardest skill isn’t getting AI to generate code – it’s catching when that confident answer is completely wrong.
The problem is pattern matching. These tools pull from training data without understanding your specific business rules or architectural constraints. They create code that compiles perfectly but fails spectacularly because they’re mimicking patterns that don’t fit your situation.
I’ve learned to watch for two red flags. First, when the solution feels unnecessarily clever for what should be simple logic. AI often overcomplicates because it’s matching against elaborate examples instead of finding the direct path. Second, when it confidently handles edge cases. That’s exactly where these systems are weakest – they’re trained on common examples, not unusual scenarios.
The scary part? Research shows 40% of AI-generated code contains vulnerabilities. It looks plausible but calls functions that don’t exist or violates your internal rules.
Here’s what actually works: treat AI like a productivity multiplier for problems you already understand. I write the pseudocode and define the architecture. Then I let AI handle the mechanical parts while I oversee the logic and integration.
Break complex tasks into smaller chunks. Give it context about your conventions and constraints. The more you narrow the solution space, the better the output.
Teams need about 11 weeks to get good at this. You’re building pattern reco
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