Except prompts and LLM's are not predictable and experienced programmer is. Ditto with true classical AI Lisps with constraints based solvers, be under Common L

Except prompts and LLM's are not predictable and experienced programmer is. Ditto with true classical AI Lisps with constraints based solvers, be under Common Lisp, be under custom Lispen such as Zenlisp where everything it's built over few axioms: https://t3x.org/zsp/index.html https://t3x.org/zsp/index.html With LLM's you will often lack predictability. If there's any, of course. Because more than I once I had to correct these over trivial errors on TCL, and they often lack cohesion between different answers. That was solved even under virtual machines for text adventures such as the ZMachine, where a clear relation between objects was pretty much defined from the start and thus a playable world emerged from few rules with the objects themselves, not something built from the start. When you define attributes for objects in a text adventure, it will map the language 1:1 to the virtual machine, and it will behave in a predictable way. You don't need a 600 page with ANSI C standards+POSIX, GLibc and > 3000 pages long books with the AMD64/i386 ISA's in order to predict a basic behaviour. It's there. Can LLM's get this? No, by design. They are like huge word predictors with eidetic memory. They might somehow be slightly good on interpolating, but they are useless extrapolating. They don't understand semantics. OTOH, the Inform6 language tageting the ZMachine interpreter has objects with implicit behaviour in their syntax and a basic syntax parser for the actions from the users. That adds a bit of context generated between the relations of the objects. The rest it's just decorated descriptions from the programmers, where the ingame answer can be changed once you drop some kind of objects and the like. Cosmetic changes in the end, because internally there's mapped a action which is indistinguisable from the vanilla output from the Inform6 English library. And Gen-Z ers don't understand that when older people tell them that no LLM will be close to a designed game from a programmer, be in Inform6 or Inform7. Because an LLM's will often mix named input, named output and the implicit named object.