> Non-determinism in LLMs is currently a feature and introduced consciously. Even if it wasn't, you would have to lock yourself on a specific model, since any future update would necessarily be a possibly breaking change

What I'm suggesting is a way to lock the model and then be able to have it revert to that state to re-interpret a set of prompts deterministically. When exploring, it can still branch non-deterministically. But once you've found a solution that works, you want the degrees of freedom to be limited.

> You'll never get this with an LLM because its very premise is using natural language, which is ambiguous

That's the point of locking the model. You need the prompts and the interpreter.

> That's the point of locking the model. You need the prompts and the interpreter.

This still doesn't seem to work for me:

- even after locking the LLM state you still need to understand how it processes your input, which is a task nobody has been able to do yet. Even worse, this can only happen after locking it, so it needs to be done for every project.

- the prompt is still ambiguous, so either you need to refine it to the point it becomes more similar to a programming language or you need an unlimited set of rules for how it should be disambiguated, which an auditor needs to learn. This makes the job of the auditor much harder and error prone.