4 ms·

I think what people call 'AI slop' is probably just unedited content. I actually like this content quite a bit. Rather than whether it was made with AI, I think

by jdw64 2mo ago

I think what people call 'AI slop' is probably just unedited content. I actually like this content quite a bit. Rather than whether it was made with AI, I think what matters is whether it accurately matches the specification

saidinesh5 2mo ago
Not just unedited but what feels like low information density... For eg. If you're generating a whole document with a single prompt, say "write a document on how to implement this" vs. "Find various approaches to implement this. These are the approaches we already have. These are the constraints we currently have." Then verify the generated text, remove useless hallucinations. Use the same AI to verify the generated output.
- jdw64 2mo ago
  Actually, isn't GPT-generated(or Gen AI) text much more information-dense than human writing? The challenge with AI prompts is that they don't produce consistent results due to various factors—from AI memory to Agent.md and other influences. I think the difference between AI and human writing is density. When AI generates informational content, it tends to have much deeper density. But where that density is directed seems to depend on the prompt. For example, if you say 'write a blog post automatically,' it tends to produce low-density, verbose text. But if you say 'find counterexamples based on this paper and that paper,' it generates highly dense sentences.
  - koiueo 2mo ago
    After I implement some feature, I often ask AI to write an ADR. The code is there, I just mention a few alternatives considered if not obvious from the conversation. Always I ask the LLM to drop the prose and reduce the content by 60%. And it does. Then I go, and edit manually, and often trim it almost twice on top of that. LLMs are professional bullshitters. Like real organic bullshitters they will mask the lack of real understanding with prose decorations.
    - jdw64 2mo ago
      Honestly, it's a workflow issue, so there is no single right answer. I agree that an ADR should be concise, for example. However, if your user memory or custom instructions are already set to prefer conciseness, the information density will naturally be high. In my opinion, the fact that an AI adds rhetorical flourishes and unnecessary elaboration alongside essential information is fundamentally a configuration issue. Furthermore, I suspect what you are referring to is its tendency to output overly accommodating explanations or mechanically neutral phrasing. However, I believe this changes completely if you provide sufficient source material. I think AI is capable of highly complex logical development. I felt this, for instance, when looking at Terence Tao's conversation logs with AI. I consider using AI to be like pouring water into a tank. If you build the "tank" using academic paper data or strict constraints as your input, it fills that tank with water of much higher purity than most humans could. In fact, it produces drafts of higher purity than if I were to write them myself. The reason I think this is simple. If standard AI outputs were inherently illogical, there would be no way to explain why it is showing such outstanding results in mathematics, the most logical of all disciplines. Based on AI papers, my understanding is that the model maps to the word with the highest probability in the semantic space for the next token. Because it selects the semantic word with the highest probability, it completes the sentence based on the statistical likelihood in its dataset following that specific context. Naturally, if you use semantically deep words in your prompt, the output becomes equally deep. Humans are fundamentally inconsistent in maintaining this balance across different domains, but AI operates with perfect homogeneity. An LLM's core mechanism is predicting the probability distribution of the next token conditioned on the current context, combined with techniques like sampling. However, when you use formal terminology commonly found in academic papers or words with deep semantic weight, the subsequent sentences and structural techniques actually unfold in a highly rigorous and logical manner. In fact, if we define being "logical" as "faithfully adhering to a procedural development without logical leaps," then I believe LLMs are more logical than humans. Humans can write at length about subjects they know well, but they falter in areas they do not. AI, on the other hand, can write about other fields with the exact same depth as my own area of expertise, to the point where it eventually generates code that even I cannot understand. Conversely, if AI is truly nothing more than a "bullshitter," are its recent achievements in mathematics simply a scam? I don't believe that's the case at all. Ultimately, it is true that our experience varies depending on our workflow and our own expertise. However, I have already seen too much proof to simply dismiss it as bullshit.
dragonwriter 2mo ago
It depends which people. For a very large contingent “slop” describes ALL GenAI output and is a more of a moral commentary on the use of the technology than an assessment of its qualitative features (it is, really, a statement that the use of AI makes it morally unacceptable to even consider its features.) For others, sure, both “AI” and “slop" narrow rhe scope compared to the other one used alone.

by jdw64 2mo ago

saidinesh5 2mo ago

Not just unedited but what feels like low information density... For eg. If you're generating a whole document with a single prompt, say "write a document on how to implement this" vs. "Find various approaches to implement this. These are the approaches we already have. These are the constraints we currently have." Then verify the generated text, remove useless hallucinations. Use the same AI to verify the generated output.

jdw64 2mo ago
Actually, isn't GPT-generated(or Gen AI) text much more information-dense than human writing? The challenge with AI prompts is that they don't produce consistent results due to various factors—from AI memory to Agent.md and other influences. I think the difference between AI and human writing is density. When AI generates informational content, it tends to have much deeper density. But where that density is directed seems to depend on the prompt. For example, if you say 'write a blog post automatically,' it tends to produce low-density, verbose text. But if you say 'find counterexamples based on this paper and that paper,' it generates highly dense sentences.
- koiueo 2mo ago
  After I implement some feature, I often ask AI to write an ADR. The code is there, I just mention a few alternatives considered if not obvious from the conversation. Always I ask the LLM to drop the prose and reduce the content by 60%. And it does. Then I go, and edit manually, and often trim it almost twice on top of that. LLMs are professional bullshitters. Like real organic bullshitters they will mask the lack of real understanding with prose decorations.
  - jdw64 2mo ago
    Honestly, it's a workflow issue, so there is no single right answer. I agree that an ADR should be concise, for example. However, if your user memory or custom instructions are already set to prefer conciseness, the information density will naturally be high. In my opinion, the fact that an AI adds rhetorical flourishes and unnecessary elaboration alongside essential information is fundamentally a configuration issue. Furthermore, I suspect what you are referring to is its tendency to output overly accommodating explanations or mechanically neutral phrasing. However, I believe this changes completely if you provide sufficient source material. I think AI is capable of highly complex logical development. I felt this, for instance, when looking at Terence Tao's conversation logs with AI. I consider using AI to be like pouring water into a tank. If you build the "tank" using academic paper data or strict constraints as your input, it fills that tank with water of much higher purity than most humans could. In fact, it produces drafts of higher purity than if I were to write them myself. The reason I think this is simple. If standard AI outputs were inherently illogical, there would be no way to explain why it is showing such outstanding results in mathematics, the most logical of all disciplines. Based on AI papers, my understanding is that the model maps to the word with the highest probability in the semantic space for the next token. Because it selects the semantic word with the highest probability, it completes the sentence based on the statistical likelihood in its dataset following that specific context. Naturally, if you use semantically deep words in your prompt, the output becomes equally deep. Humans are fundamentally inconsistent in maintaining this balance across different domains, but AI operates with perfect homogeneity. An LLM's core mechanism is predicting the probability distribution of the next token conditioned on the current context, combined with techniques like sampling. However, when you use formal terminology commonly found in academic papers or words with deep semantic weight, the subsequent sentences and structural techniques actually unfold in a highly rigorous and logical manner. In fact, if we define being "logical" as "faithfully adhering to a procedural development without logical leaps," then I believe LLMs are more logical than humans. Humans can write at length about subjects they know well, but they falter in areas they do not. AI, on the other hand, can write about other fields with the exact same depth as my own area of expertise, to the point where it eventually generates code that even I cannot understand. Conversely, if AI is truly nothing more than a "bullshitter," are its recent achievements in mathematics simply a scam? I don't believe that's the case at all. Ultimately, it is true that our experience varies depending on our workflow and our own expertise. However, I have already seen too much proof to simply dismiss it as bullshit.

dragonwriter 2mo ago

It depends which people. For a very large contingent “slop” describes ALL GenAI output and is a more of a moral commentary on the use of the technology than an assessment of its qualitative features (it is, really, a statement that the use of AI makes it morally unacceptable to even consider its features.) For others, sure, both “AI” and “slop" narrow rhe scope compared to the other one used alone.