They can't, they usually just don't understand the code enough to notice the issues immediately.
The perceived quality of LLM answers is inversely proportional to the user's understanding of the topic they're asking about.
When I'm using llama.vim, like 40% of what it writes in a 4-5 line completion is exactly what I'd write. 20-30% is stuff that I wouldn't judge coming from someone else, so I usually accept it. And 30-40% is garbage... but I just write a comment or a couple of lines, instead, and then reroll the dice.
It's like working through a junior engineer, except the junior engineer types a new solution instantly. I can get down to alternating between mashing tab and writing tricky lines.
Prompt based stuff, like "extract the filtering part from all API endpoints in folder abc/xyz. Find a suitable abstraction and put this function into filter-utils.codefile"
My comment was more about just asking questions on how to do things you're totally clueless about, in the form of "how do I implement X using Y?" for example. I've found that, as a general rule, if I can't find the answer to that question myself in a minute or two of googling, LLMs can't answer it either the majority of the time. This would be fine if they said "I don't know how to do that" or "I don't believe that's possible" but no, they will confidently make up code that doesn't work using interfaces that don't exist, which usually ends up wasting my time.
But if I'm writing out a bunch of linear algebra, I get a lot of useful completions and avoid tediousness.
I've settled on Qwen2.5.1-Coder-7B-Instruct-Q6_K_L.gguf-- so not even a very big model.
A few months ago I tried to do a small project with Langchain. I'm a professional software developer, but it was my first Python project. So I tried to use a lot of AI generated code.
I was really surprised that AI couldn't do much more than in the examples. Whenever I had some things to solve that were not supported with the Langchain abstractions it just started to hallucinate Langchain methods that didn't exist, instead of suggesting some code to actually solve it. I had to figure it out by myself, the glue code I had to hack together wasn't pretty, but it worked. And I learned not to use Langchain ever again :)