alt Hacker News> But they generate objectively bad text.
By default, yes. That's exactly the same for code: by default, even with planning and patient nudging towards best practices, you get passable code at best. Not elegant, not performant, and not particularly readable, either. Basically, an uninspired salaryman type of code.
In both domains, you can get much better results in some specific circumstances. Prompt, skills, memory, and the task must align, but when they do, you can get good quality building blocks that you can then work with. It's crucial to recognize the instances where there's a chance of getting better-than-average results and ones where you could dump your whole week of tokens into and still end up with a mess. This is a skill of the LLM operator.
Once get that skill, the only issue is capitalizing on it: basically, how you fit handling of the generated building blocks into your workflow. If it's seamless, it can be a big win. If it's not - yeah, writing by hand from scratch is often better.
I’ve certainly heard from LLM operators that there is a lot of skill in developing prompts and guiding their agents, but frankly it doesn’t seem like there is much benefit at that point unless you’re willing to accept mediocre results or spend lots of time rewriting and trying to find errors.
Given the examples of writing I’ve seen so far from them I’m skeptical that LLMs are useful for any writing where quality and truth are important.