Arguably it’s only unnecessary when the value range is enforced by the programming language (types). The structure of who calls what in the code can change, and suddenly larger values can appear. You don’t want to have to perform whole-program analysis on every change to check whether all assumptions made in the past still hold. That’s also a source of vulnerabilities: assumptions that held when the code was written, but stopped holding sometime later.

That’s why I think code needs to document all its assumptions, and in a way that is checked by the compiler to the extent possible. If you document that the values have restricted range, then the AI should take it into account.

Types is an option. Another option would be tests (and in this case mutation testing).

My main point still stands = LLMs code defensively catering for unknowns that are not loaded into context.

And the day you add a way to batch import request through another code path, you may forget the validation.

Having it in the lower layer is correct, a proper optimizing compiler should be able to detect there's a single code path to the function and that the checks have already been done, skipping the second check.

Well - this would be an example of defensive programming and by that logic catering for this "what if" is the way to go. LLMs were correct all along!

Alternatively, the code could be written in the style of parse, don't validate[1]. However I've never had a coding LLM preemptively suggest that.

[1]: https://lexi-lambda.github.io/blog/2019/11/05/parse-don-t-va...