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Comment by eithed | Hacker News Reader

An example to what I'm talking about: I have application that takes two inputs from request. I've put in validation on request values to deny values that are greater than 100. In my code I'm adding these values. LLM can say "cater for addition of these two numbers, because they might go over MAX_INT and result in overflow". This is a situation that won't happen. LLM can recommend - add assertions. That's unnecessary.

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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.

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...

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!

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 be taking 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.

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