Most developers struggle with AI coding tools while others dominate. After 30 years coding, I found it’s not about the smartest model – it’s how you use them.
The story
I just spent the morning digging into something that’s been bugging me: why do some developers crush it with AI coding assistants while others struggle?
Turns out, it’s not about picking the “smartest” model.
After 30 years writing code and 8 years working with AI, here’s what I’ve learned: the real skill is in how you use these tools, not which one you choose.
The difference-makers are surprisingly simple:
Context is everything. The best results come from tools that understand your entire codebase, not just the file you have open. When an AI assistant can see your monorepo, build logs, and PR history, it stops making rookie mistakes.
Prompts matter more than you think. “Write a sorting function” gets you garbage. “Write a Python function that sorts a list of integers using quicksort” gets you something useful. Be specific. Include constraints. Describe what you actually want.
Break big problems into chunks. Feed the AI smaller, well-defined tasks. Combine the results. This cuts errors and makes reviewing way easier.
Always review everything. AI can introduce subtle bugs and security holes. Treat generated code like you would a junior developer’s work – it needs eyes on it before shipping.
Document your context. At the end of each session, save your architecture notes, dependency versions, and key decisions. Next time you fire up the AI, it’ll pick up right where you left off instead of starting from scratch.
The wild part? 99% of developers report time savings with these tools, but only 16% actually use them at work. Security concerns and workflow friction are hol
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