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Why AI Isn’t Great at Writing Code from Scratch
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The hype around large language models (LLMs) has made it feel like AI could swoop in, save developers from endless coding sessions, and maybe even write that dream project you’ve had in mind. Spoiler alert: it won’t. While LLMs are surprisingly good at some parts of coding, they fall flat when it comes to creating entire software systems from scratch.
It Doesn’t Get Your Vision (Unless You Spell It Out in Painful Detail)
Humans are great at turning vague ideas into working systems. You might tell a teammate, “I want a simple chat app,” and they’ll ask about real-time updates, user authentication, and whether it should handle GIFs. An LLM? It’ll give you a basic chat app that may not even compile/run, with no idea whether it meets your actual needs unless you guide it every step of the way.
Where It Works Better
LLMs thrive when you need help filling in the blanks for common patterns. Already have a half-built app but don’t want to write out that boring login flow? Perfect – ask an AI to handle the boilerplate.
It Doesn’t Handle Ambiguity or Trade-offs Well
Building software often means making choices – should your service optimize for speed or memory? Should your app be real-time or asynchronous? AI doesn’t weigh trade-offs because it doesn’t really think; it just regurgitates code based on patterns it has seen before.
LLMs are fantastic at giving you options. Need a quick list of database indexing strategies? It’ll give you some to choose from. But you still have to decide what’s best for your project.
Specialized Domains? Forget It
In niche areas, like writing high-performance code for embedded systems or designing a custom game engine, experience matters. AI lacks that experience – it’s trained on general-purpose data, so it struggles when the task requires deep, domain-specific knowledge.
On the flip side, LLMs are great when you need general-purpose code: API integration, data parsing, or converting…
