🔍 4 Pitfalls in using AI to build software
AI has evolved into a clear productivity multiplier for developers. Yet some remain skeptical, frustrated by its limitations. The issue often isn't the tool - it's misaligned expectations. Like any powerful technology, success comes from leveraging its strengths rather than fixating on what it can't do.
Through working extensively with AI, I've observed common patterns that hold developers back from realizing its full potential. By understanding these traps, we can better align our expectations and achieve more meaningful outcomes.
Problem definition matters. Keywords play such a huge impact it can change the nature of the implementation with or without AI. For example, if I drew a circle, and I stated, what is this familiar object? You would immediately state it's a circle. But if I had instead asked, what is this object? Perhaps you would find it odd why I would be asking some a basic question, one I already know the answer to. Doubt would creep, perhaps some red flags, resulting in you perhaps asking more questions. Consider a third approach, what if I asked you instead, what is this unfamiliar object? This would certainly make you step back and think. How I framed the problem, impacted how you derived your outcome.
Picture a chef asking a kitchen assistant to "just fix dinner service." Even with access to every ingredient and recipe, the assistant can't replace the chef's experience, judgment, and understanding of their kitchen's unique challenges. Similarly, feeding an AI your entire codebase won't magically solve architectural problems or replace developer intuition.
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