More on the Blender file format: https://fossies.org/linux/blender/doc/blender_file_format/my...
LLMs are good at writing copy that sounds accurate and creative enough, and there are known techniques to improve that (such as generating an outline first, then generating each section separately). If you then give them a list of templates, and written examples of what they are used for, the LLM is able to pick one that's a suitable match. But this is all just probability, there's no real creativity here.
Earlier this year I played around with trying to have GPT-3 directly output an SVG given a prompt for a simple design task (a poster for a school sports day), and the results were pretty bad. It was able to generate a syntantically coreect SVG, but the design was terrible. Think using #F00 and #0F0 as colours, placing elements outside the screen boundaries, layering elements so they are overlapping.
This was before GPT-4, so it would be interesting to repeat that now. Given the success people are having with GPT-4V, I feel that it could just be a matter of needing to train a model to do this specific task.
That pretty much matches my experience working with NN's and LLM's
The only trick is that there has to be enough Blender Python code to train the LLM on.
While it generates a lot of code that initially makes sense, when you use the code, you get a jumbled block.
But also I suspect there just isn't that much openscad code in the training data, and the semantics are different enough to python or any of the other languages that are well-represented that it struggles.