Thank you for this interesting discussion so far.
Using an LLM to generate a PLECS file directly is something that I would consider a possible workflow. As mentioned above, PLECS Standalone files are text-based and relatively simple for an LLM to interpret (and partly also for humans).
As a quick test, I took our most basic boost converter demo model, uploaded it to ChatGPT, and asked it to optimize the model for 250 W output power. Even though I accidentally wrote “buck converter” in my prompt, ChatGPT corrected me, derived the relevant equations, and explained the design steps. It then generated a modified PLECS Standalone file which ran successfully and delivered 250 W at the output.
Note that this test was performed using the browser version of ChatGPT without any specific training or customization.
TL;DR: Using LLMs for model generation is definitely a possible workflow.
FMUs, on the other hand, are meant to exchange models (Model Exchange) or even solvers (Co-Simulation) between different simulation tools. Using them as a mechanism for AI-based model generation is not something I would expect to see.
If you are interested in FMUs, have a look at this forum post: FMU workflow in PLECS