I'm not sure I agree that LLMs solve for the statistical mean

But more importantly: isn't what you call "intent" just a series of optimization goals? You want your code to satisfy the constraint of being correct™, while also maximizing various other goals like being maintainable, easy to understand, having few lines of code, as little tight coupling as possible, etc. Goals that often conflict, but when given two implementations you could likely tell which hits the better tradeoff (in your engineering experience)

Those are all things that theoretically - with a tight enough specification and enough compute - a constraint solver could solve. No human intent necessary.

The issue is more that we can't fully specify all those side goals, and even if we could the LLM would struggle following them. A classic paperclip maximizer problem (where nobody told the paperclip maximizer to keep the planet inhabitable and all the other side conditions we implicitly assume)

This is a weird framing, but the user is the "intent". If you can produce "great code" by yourself (whatever that is), then you can get your agent to produce it for you. If you are talking about vibe-coding then yeah, it is going to be trash

If you built a task management system, you'd have very different code bases depending on whether it's for internal use at a mid-size development org or as a SaaS.

So I wonder whether, in your experience, the results you've seen, could have improved by providing sufficient context? - or what context was given.

I.e. if you have the agent that same context, as one of your colleagues would have/require to solve a problem.

> because great code requires intent

If can put properly engineered intent in the prompt that is verifiable, it works wonders. Anything that can defaults to the llm doing its way, you're right, it just can't converge to good, not with proper constraints.

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