And another aspect is that, at least in the realm of coding, we're trying to get these models to write code in a way humans do it. But I'd argue that it's not really an optimal approach because models have different strength. The biggest limitation they have is that they struggle with large contexts, but if given a small and focused task, even small models can handle it well. So, we could move to structuring programs out of small isolated components that can be reasoned about independently. There are already tools like workflow engines that do this sort of stuff, they just never caught on with human coders because they require more ceremony. But I think that viewing a program as a state graph would be a really nice way for humans to be able to tell whether the semantics are correct, and then the LLM could implement each node in the graph as a small isolated task that can be verified fairly easily.
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