4 ms·

I do not expect the LLMs to do everything. They are assistants. You still bear the responsibility of stitching everything together into a cohesive whole. Make c

by sieve 2mo ago

I do not expect the LLMs to do everything. They are assistants. You still bear the responsibility of stitching everything together into a cohesive whole. Make changes as required. That is where expectations differ I guess.

grey-area 2mo ago
But they generate objectively bad text. Why do you need an assistant to do that? Perhaps for some uses (say a summary of some business document, or generating code which is then discarded or edited) that's acceptable. For literature, do you really feel they are useful tools? So many changes would be required IMO that you're better off just writing the story you want to tell.
- sieve 2mo ago
  > But they generate objectively bad text. They don't. I don't think people are taking full advantage of their capabilities. They produce perfectly functional prose. Sometimes, they produce something genuinely amazing to read. But that is quite rare. People should stop expecting brilliant prose from LLMs without doing the preparatory work themselves first. Very few human authors can manage that in any case
  - illwrks 2mo ago
    I’d partially agree. They can generate very good descriptive text however the issue I’ve found is that when something is implied it’s not fully ‘understood’ in the context of everything else and then gets ignored by the agent.
    - sieve 2mo ago
      My solution to this, as the human-in-the-loop, is pretty simple. If the generated draft is missing something that was implicit in the chapter outline, I ask the LLM to incorporate it and rewrite the para/section. Then I fix the outline to make the point explicit so that a reroll does not make the same mistake again
  - grey-area 2mo ago
    Show me some examples of good long form text please. Your hedging in reply (perfectly functional) does not inspire confidence.
    - sieve 2mo ago
      This is something from early 2025. The situation and my own way of driving the models has improved somewhat in the intervening period. Variations on a Theme of Saki https://gist.github.com/s-i-e-v-e/b4d696bfb08488aeb893cce3a4c174cd https://gist.github.com/s-i-e-v-e/b4d696bfb08488aeb893cce3a4...
      - grey-area 2mo ago
        I’m sorry but this isn’t very good, problems from a cursory look: Stuffed full of adjectives, a common problem when LLMs attempt to make the text literary. Stuffed full of incidental detail (window inexplicably open), which doesn’t quite make sense - why would you leave a window open for people to return? Things which suddenly loom large in the narrative without any previous mention (roaring fire). Overuse of certain mechanisms (for example here ellipsis). Meanders without any discernable goal from one scene to the next. So in short, it’s a tale told by an idiot savant, full of sound and fury, signifying nothing. Take this sentence as an example: A biting incident, if I'm to be perfectly honest. Involving… several toes. The ellipsis just gets in the way, the ‘perfectly honest’ likewise and this should be one better formed sentence. The several toes bit is overly explicit without being clear (he lost several toes, or toes were bitten?). Most of the text is like this - infuriatingly vague, clumsy in construction and full of non-sequiturs. Compare this with real writing: https://archive.org/stream/GrahamGreeneShorts/21%20Stories_djvu.txt https://archive.org/stream/GrahamGreeneShorts/21%20Stories_d... https://www.gutenberg.org/files/3077/3077-h/3077-h.htm#link2H_4_0003 https://www.gutenberg.org/files/3077/3077-h/3077-h.htm#link2...
        sieve 2mo ago
        Did you read the original by Saki (Exhibit 12)? Ideally, you should read all 14 exhibits. Each has been produced by a different LLM, some local, others frontier models of the time. E4 = Claude E6 = ChatGPT E8 = Mistral E11 = DeepSeek V3 E12 = Original
- klibertp 2mo ago
  > 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.
  - grey-area 2mo ago
    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.
    - klibertp 2mo ago
      > doesn’t seem like there is much benefit at that point That depends on the problem you're trying to solve. I found it to be of great benefit when trying to overcome "writer block". Mediocre prose - but at least some prose to work with, tailored to what you want to write - can be a much better starting point than a blank sheet of paper. Also, editing can be fast: the output is mediocre in predictable ways; it's not hard to quickly identify everything of value in a paragraph and rewrite the rest. It probably takes longer than writing the paragraph from scratch - but only if you already know precisely what you want to write, which is far from "always" when writing prose, IME.

by sieve 2mo ago

grey-area 2mo ago

But they generate objectively bad text. Why do you need an assistant to do that? Perhaps for some uses (say a summary of some business document, or generating code which is then discarded or edited) that's acceptable. For literature, do you really feel they are useful tools? So many changes would be required IMO that you're better off just writing the story you want to tell.

sieve 2mo ago
> But they generate objectively bad text. They don't. I don't think people are taking full advantage of their capabilities. They produce perfectly functional prose. Sometimes, they produce something genuinely amazing to read. But that is quite rare. People should stop expecting brilliant prose from LLMs without doing the preparatory work themselves first. Very few human authors can manage that in any case
- illwrks 2mo ago
  I’d partially agree. They can generate very good descriptive text however the issue I’ve found is that when something is implied it’s not fully ‘understood’ in the context of everything else and then gets ignored by the agent.
  - sieve 2mo ago
    My solution to this, as the human-in-the-loop, is pretty simple. If the generated draft is missing something that was implicit in the chapter outline, I ask the LLM to incorporate it and rewrite the para/section. Then I fix the outline to make the point explicit so that a reroll does not make the same mistake again
- grey-area 2mo ago
  Show me some examples of good long form text please. Your hedging in reply (perfectly functional) does not inspire confidence.
  - sieve 2mo ago
    This is something from early 2025. The situation and my own way of driving the models has improved somewhat in the intervening period. Variations on a Theme of Saki https://gist.github.com/s-i-e-v-e/b4d696bfb08488aeb893cce3a4c174cd https://gist.github.com/s-i-e-v-e/b4d696bfb08488aeb893cce3a4...
    - grey-area 2mo ago
      I’m sorry but this isn’t very good, problems from a cursory look: Stuffed full of adjectives, a common problem when LLMs attempt to make the text literary. Stuffed full of incidental detail (window inexplicably open), which doesn’t quite make sense - why would you leave a window open for people to return? Things which suddenly loom large in the narrative without any previous mention (roaring fire). Overuse of certain mechanisms (for example here ellipsis). Meanders without any discernable goal from one scene to the next. So in short, it’s a tale told by an idiot savant, full of sound and fury, signifying nothing. Take this sentence as an example: A biting incident, if I'm to be perfectly honest. Involving… several toes. The ellipsis just gets in the way, the ‘perfectly honest’ likewise and this should be one better formed sentence. The several toes bit is overly explicit without being clear (he lost several toes, or toes were bitten?). Most of the text is like this - infuriatingly vague, clumsy in construction and full of non-sequiturs. Compare this with real writing: https://archive.org/stream/GrahamGreeneShorts/21%20Stories_djvu.txt https://archive.org/stream/GrahamGreeneShorts/21%20Stories_d... https://www.gutenberg.org/files/3077/3077-h/3077-h.htm#link2H_4_0003 https://www.gutenberg.org/files/3077/3077-h/3077-h.htm#link2...
      - sieve 2mo ago
        Did you read the original by Saki (Exhibit 12)? Ideally, you should read all 14 exhibits. Each has been produced by a different LLM, some local, others frontier models of the time. E4 = Claude E6 = ChatGPT E8 = Mistral E11 = DeepSeek V3 E12 = Original
klibertp 2mo ago
> 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.
- grey-area 2mo ago
  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.
  - klibertp 2mo ago
    > doesn’t seem like there is much benefit at that point That depends on the problem you're trying to solve. I found it to be of great benefit when trying to overcome "writer block". Mediocre prose - but at least some prose to work with, tailored to what you want to write - can be a much better starting point than a blank sheet of paper. Also, editing can be fast: the output is mediocre in predictable ways; it's not hard to quickly identify everything of value in a paragraph and rewrite the rest. It probably takes longer than writing the paragraph from scratch - but only if you already know precisely what you want to write, which is far from "always" when writing prose, IME.