This is a contradiction. If you don't understand the code, you cannot in fact evaluate that it works correctly. If that's the approach you are taking, you are doing sloppy work that is going to blow up in your face sooner or later.
I believe this is where the huge divide in perceived AI productivity in SW comes from. It’s folks working on low-understanding-required domains talking to folks working on high-understanding-required domains.
My experience has been that small-ish projects like these are the most likely to contain code with bond-villain level of complexity (and success).
More than once have I been stuck going on Da Vinci Code-esque adventures to uncover bugs in prototypes years after the fact because the business pivoted away from what it was trying to do and later decided to pivot back, only to discover that despite the prototype and systems/libraries it worked with not changing, somehow it mysteriously doesn't work at all or it fails in convoluted ways despite being perfectly functional when it was originally shelved.
Those things shouldn't be hugely complex. They sound actually very simple.