Comment by jbergqvist
9 hours ago
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.
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.’