alt Hacker NewsDefinitely agree that performance optimization is a good use case for LLMs. Here you have both a measurable goal / objective function and guardrails against functional regressions. It kind of closes the loop in that regard.
One thing however is a test suite is not usually exhaustive in the sense that any code that passes the tests is valid. Usually tests are more complimentary in nature. Therefore you could still possibly get code degradation, potentially.
> One thing however is a test suite is not usually exhaustive in the sense that any code that passes the tests is valid. Usually tests are more complimentary in nature.
Not in the world of AI - if your tests don't catch any known issues, the problem is the tests aren't comprehensive enough. There's no excuse at this point not to have an incredibly comprehensive test suite, to go with your other agent feedback loop constraints