If the code these LLMs produce has logical errors and bugs, I have formed the opinion that this is mostly user error. LLMs produce great code if they're used pr

If the code these LLMs produce has logical errors and bugs, I have formed the opinion that this is mostly user error. LLMs produce great code if they're used properly. By that I mean: strictly control the maximum level of complexity that you're asking the LLMs to solve. GPT-4o can only handle rudimentary coding tasks. So I only ask it rudimentary questions. And it's rarely wrong or bugged. o1-2024-12-17 is much better, it can handle "mid" complexity tasks, it's usually never wrong on those, but a step above and it will output buggy code. This is subjective and not perfect, but when you use the models more you can get a sense for where it starts falling apart and stay below that threshold. This means chunking your problem into multiple prompts, where each chunk is nearing the maximum level of complexity that the model can handle.