You know that feeling when someone gives you directions with total confidence – and you end up at a dead end? That’s AI coding assistants in a nutshell.
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You know that feeling when someone gives you directions with total confidence – and you end up at a dead end?
That’s AI coding assistants in a nutshell.
After thirty years writing code and eight years building AI solutions, I’ve learned the hard way: an AI’s confidence means nothing. It’ll hallucinate APIs that don’t exist, suggest deprecated libraries, or hand you code that looks perfect but breaks three layers deep.
The trick isn’t avoiding AI – it’s knowing when to ignore it.
I use a simple mental checklist. If an answer comes back too smooth or introduces unfamiliar names, I stop and verify. If it touches security, authentication, or dependencies, I cross-check everything. If it feels generic or skips the messy reality of integration, I slow down and test edge cases like I’m reviewing a junior dev’s pull request.
Prompt engineering helps. I break problems into smaller chunks, specify constraints, and chain prompts – feeding outputs back for review or asking for alternatives. That catches errors AI missed the first time.
But honestly, the best defense is what I call “vibe coding.” If something feels too neat, too fast, or glosses over infrastructure and error handling, my gut kicks in. That instinct – built over decades – tells me when to dig deeper.
AI is useful. It speeds up my work. But I treat it like a new hire: capable, but unproven. I orchestrate a loop of prompting, reviewing, testing, and iterating. I never outsource my judgment.
The real skill isn’t just using AI. It’s knowing when its confidence is wrong – and acting accordingly.
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