Well, I do need to be a bit vague. The main DSL has about 50 instructions corresponding to actions to be performed. There's also another different sub-DSL, with about 25 instructions, to represent key features of the domain model, that allows particular scenarios to be defined and then recognized when executing.

Both DSLs are almost entirely linear and declarative, so there's no nested structure, and the only control flow is a conditional branch instruction in the top-level DSL, to support conditional execution and looping. The UI essentially acts as a wizard, so that users don't have to deal with low-level detail.

There are various ideas for the GPT model, including suggesting instructions when creating a program, self-healing when something breaks, and finally generating programs from scratch based on data that we happen to already collect anyway.

NLP will probably end up being part of it as well - for that, we'd probably use the fine-tuning approach with an existing language model as you suggested.