Comment by ben_w
17 hours ago
All the problems you list are true, but the solutions not so much.
I've seen this problem with humans even back at university when it was the lecturer's own example attempting to illustrate the value of formal methods and verification.
I would say the solution is neither "get humans to do it" nor "do it before writing code", but rather "get multiple different minds involved to check each other's blind spots, and no matter how many AI models you throw at it they only count as one mind even when they're from different providers". Human tests and AI code, AI tests and human code, having humans do code reviews of AI code or vice-versa, all good. Two different humans usually have different blind spots, though even then I've seen some humans bully their way into being the only voice in the room with the full support of their boss, not that AI would help with that.
This is actually my big gripe about chatbot coding agents. They are trained on human preference and thus they optimize for errors that are in our blind spots.
I don't think people take this subtly seriously enough. Unless we have an /objective/ ground truth we end up proxying our optimization. So we don't optimize for code that /is/ correct, we optimize for code that /looks/ correct. It may seem like a subtle difference but it is critical.
The big difference is when they make errors they are errors that are more likely to be difficult for humans to detect.
Good tools should complement tool users. Fill in gaps. But as we've been trying to train agents to replace humans we are not focusing on this distinction. I want my coding agent to make errors that are obvious to me just as I want errors I make to be obvious to it (or for it to be optimized to detect errors I make)
Maybe this is because humans have good intuition to know the difference between us. But this type of intuition does not work on the behaviour of LLMs.