[jbergqvist]

I've found that spending most of my time on design before any code gets written makes the biggest difference.

The way I think about it: the model has a probability distribution over all possible implementations, shaped by its training data. Given a vague prompt, that distribution is wide and you're likely to get something generic. As you iterate on a design with the model (really just refining the context), the distribution narrows towards a subset of implementations. By the time the model writes code, you've constrained the space enough that most of what it produces is actually what you want.

[ugtr3]

Yea but that design part is the most expensive part. The code generation is pretty trivial - the advantage of llm’s is the power to search through pre-trained information spaces - much faster than a human could. Issue is again.. probabilistic. So there’s variance.’