Yeah, I was also thinking about it A LOT.

We kinda have a little bit of it with some coding harnesses giving model access to LSP, but I think that we can insert this knowledge on a lower level if we find a clever way to somehow utilize it during sampling.

I think that there is a lot of low hanging fruit in this area.

And in general, I think that people try to use LLMs too much to solve problems that can be easily solved by cheaper (computationally), and, more importantly deterministic tools.

For example, back in the day when LLM-assisted coding just became a thing people very often complained about models generating syntactically incorrect code and inventing non-existent library methods.

Well, I, an experienced human programmer, probably would also be making syntax mistakes and inventing non-existent methods if you stripped me of my tools and made me write code in a bare text editor without syntax highlighting.

Thankfully, my IDE would autocomplete real syntax and actually existing library methods for me and immediately give me feedback if I make a mistake anyway. And all of it is achieved using reliable deterministic code without the inherent issues of statistical models.

I think that it is really inefficient to reach for an expensive and unreliable tool when a cheap and reliable tool will do.