This is a poor quality blog. They "upload" source code to chatgpt so who knows if it's in context or ragd plus it looks like they reuse the chat from a previous prose -> code session. They criticise LLM for high level diagram despite using a garbage prompt and rejecting the idea of iterating on the diagram.
Anecdotally I've had great success with code to diagram via LLM including fine details. But as with anything LLM you need to really get the context right. This can not be overemphasized. And iterate with the LLM, goodness.
As mentioned in the blog, iterating on generating a diagram from a repo kind of defeats the purpose. The information is in the repo; if the LLM isn't going to analyze it properly, you might as well just tell it exactly what to diagram (like in the previous section on "whiteboarding"). It is much more capable at that.
If you have some examples of an LLM doing better, by all means please share.
"properly" is the key word here. You've got to communicate what you want. Or at least communicate something like what your goal is from the diagram, why you want it, so the LLM knows the audience it's targeting.
Like imagine giving the same prompt (instruction, directive, task) to a human - you would in all likelihood get out a similar high level diagram because you've not provided even the slightest whiff of what you want to use the diagram for.
The blog's takeaway is essentially "LLM didn't read my mind so no good". They're tools to be used and you get out what you put in.
Anecdotally I've had great success with code to diagram via LLM including fine details. But as with anything LLM you need to really get the context right. This can not be overemphasized. And iterate with the LLM, goodness.