Comment by acyou

I think if you could directly tokenize 3D geometry and train an LLM on 3D models directly, you might get somewhere. In order to prompt it, you would need to feed it a 3D model(s), and prompts and it could give you back a different 3D model. This has been done to some extent with generative modeling pre-LLM, but I don't know of any work that takes LLM techniques applied to language and applies them to "tokenizing" 3D geometry. I suspect NVIDIA is probably working very hard on this exact problem for graphics applications.

For mechanical design, 3D modeling is highly integrative, inputs are from a vast array of poorly specified inputs with a high amount of unspecified and fluid contextual knowledge, and outputs are not well defined either. I'm not convinced that mechanical design is particularly well suited to pairing with LLM workflow. Certain aspects, sure. But 3D models and drawings that we consider "well-defined" are still usually quite poorly defined, and from necessity rely heavily on implicit assumptions.

The geometry of machine threads, for example. Are you going to have a big computer specify the position of each of the atoms in the machine thread? Even the most detailed CAD/CAM packages have thread geometry extremely loosely defined, to the point of listing the callout, and not modeling any geometry at all in many cases.

It would just be very difficult to feed enough contextual knowledge into an LLM to have the knowledge it needs to do mechanical design. Therein lies the main problem. And I will stress that it's not a training problem, it's a prompt problem, if that makes sense.