Code written in a HLL is a sufficient[1] description of the resulting program/behavior. The code, in combination with the runtime, define constraints on the behavior of the resulting program. A finished piece of HLL code encodes all the constraints the programmer desired. Presuming a 'correct' compiler/runtime, any variation in the resulting program (equivalently the behavior of an interpreter running the HLL code) varies within the boundaries of those constraints.
Code in general is also local, in the sense that small perturbation to the code has effects limited to a small and corresponding portion of the program/behavior. A change to the body of a function changes the generated machine code for that function, and nothing else[2].
Prompts provided to an LLM are neither sufficient nor local in the same way.
The inherent opacity of the LLM means we can make only probabilistic guarantees that the constraints the prompt intends to encode are reflected by the output. No theory (that we now know) can even attempt to supply such a guarantee. A given (sequence of) prompts might result in a program that happens to encode the constraints the programmer intended, but that _must_ be verified by inspection and testing.
One might argue that of course an LLM can be made to produce precisely the same output for the same input; it is itself a program after all. However, that 'reproducibility' should not convince us that the prompts + weights totally define the code any more than random.Random(1).random() being constant should cause us to declare python's .random() broken. In both cases we're looking at a single sample from a pRNG. Any variation whatsoever would result in a different generated program, with no guarantee that program would satisfy the constraints the programmer intended to encode in the prompts.
While locality falls similarly, one might point out the an agentic LLM can easily make a local change to code if asked. I would argue that an agentic LLMs prompts are not just the inputs from the user, but the entire codebase in its repo (if sparsely attended to by RAG or retrieval tool calls or w/e). The prompts _alone_ cannot be changed locally in a way that guarantees a local effect.
The prompt LLM -> program abstraction presents leaks of such volume and variety that it cannon be ignored like the code -> compiler -> program abstraction can. Continuing to make forward progress on a project requires the robot (and likely the human) attend to the generated code.
Does any of this matter? Compilers and interpreters themselves are imperfect, their formal verification is incomplete and underutilized. We have to verify properties of programs via testing anyway. And who cares if the prompts alone are insufficient? We can keep a few 100kb of code around and retrieve over it to keep the robot on track, and the human more-or-less in the loop. And if it ends up rewriting the whole thing every few iterations as it drifts, who cares?
For some projects where quality, correctness, interoperability, novelty, etc don't matter, it might be. Even in those, defining a program purely via prompts seems likely to devolve eventually into aggravation. For the rest, the end of software engineering seems to be greatly exaggerated.
[1]: loosely in the statistical sense of containing all the information the programmer was able to encode https://en.wikipedia.org/wiki/Sufficient_statistic
[2]: there're of course many tiny exceptions to this. we might be changing a function that's inlined all over the place; we might be changing something that's explicitly global state; we might vary timing of something that causes async tasks to schedule in a different order etc etc. I believe the point stands regardless.