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I must say, I do love how this comment has provoked such varying responses. My own observations about using AI to write code is that it changes my position fro

I must say, I do love how this comment has provoked such varying responses. My own observations about using AI to write code is that it changes my position from that of an author to a reviewer. And I find code review to be a much more exhausting task than writing code in the first place, especially when you have to work out how and why the AI-generated code is structured the way it is.

thegrim33 8mo ago
There's a very wide range of programming tasks of differing difficulty that people are using / trying to use it for, and a very wide range of intelligence amongst the people that are using / trying to use it, and who are evaluating its results. Hence, different people have very different takes.
seanmcdirmid 8mo ago
> especially when you have to work out how and why the AI-generated code is structured the way it is. You could just ask it? Or you don’t trust the AI to answer you honestly?
- chmod775 8mo ago
  You're anthropomorphizing. LLMs can't lie nor can they tell the truth. These concepts just don't apply to them. They also cannot tell you what they were "thinking" when they wrote a piece of code. If you "ask" them what they were thinking, you just get a plausible response, not the "intention" that may or may not have existed in some abstract form in some layer when the system selected tokens*. That information is gone at that point and the LLM has no means to turn that information into something a human could understand anyways. They simply do not have what in a human might be called metacognition. For now. There's lots of ongoing experimental research in this direction though. Chances are that when you ask an LLM about their output, you'll get the response of either someone who now recognized an issue with their work, or the likeness of someone who believes they did great work and is now defending it. Obviously this is based on the work itself being fed back through the context window, which will inform the response, and thus it may not be entirely useless, but... this is all very far removed from what a conscious being might explain about their thoughts. The closest you can currently get to this is reading the "reasoning" tokens, though even those are just some selected system output that is then fed back to inform later output. There's nothing stopping the system from "reasoning" that it should say A, but then outputting B. Example: https://i.imgur.com/e8PX84Z.png https://i.imgur.com/e8PX84Z.png * One might say that the LLM itself always considers every possible token and assigns weights to them, so there wouldn't even be a single chain of thought in the first place. More like... every possible "thought" at the same time at varying intensities.
  - pipes 8mo ago
    Or ask another model to tell you what the changes do.
    - chmod775 8mo ago
      Did you mean to reply to some other comment? I'm having trouble contextualizing your response - pardon the pun.
    - deleted 8mo ago
      [deleted]
    - cyclopeanutopia 8mo ago
      And you could first read the thing to which you are replying. Don't tell me it was too long.
      - seanmcdirmid 8mo ago
        It was too long, but thankfully AI can summarize so it’s no excuse anymore.
  - seanmcdirmid 8mo ago
    I’m not anthropomorphizing. I’ve been in many situation where the AI wrote some code some way and I had to ask why, it told me why and then we moved on to better solutions as needed. Better if it just wrote the code and its reasoning was still in context, but even if it’s not, it can usually reverse engineer what it wrote well enough. Then it’s a conversation about whether there is a better clearer way to do it, the code improves. It sounds like you either have access to bad models or you are just imagining what it’s like to use an LLM in this way and haven’t actually tried asking it why it wrote something. The only judgement you need to make is the explanation makes sense or not, not some technical or theoretical argument about where the tokens in the explanation come from. You just ask questions until you can easily verify things for yourself. Also, pretending that the LLM is still just token predicting and isn’t bringing in a lot of extra context via RAG and using extra tokens for thinking to answer a query is just way out there.
tayo42 8mo ago
Your always reviewing code though. Either a team mates pr or maybe your own code in 3 months, or some legacy thing.
- christophilus 8mo ago
  Human code is still easier to review. Also, I program 80% of the time and review PRs 20% of the time. With AI, that becomes: I review 80% of the time, and write markdown and wait 20% of the time.

thegrim33 8mo ago

There's a very wide range of programming tasks of differing difficulty that people are using / trying to use it for, and a very wide range of intelligence amongst the people that are using / trying to use it, and who are evaluating its results. Hence, different people have very different takes.

seanmcdirmid 8mo ago

> especially when you have to work out how and why the AI-generated code is structured the way it is. You could just ask it? Or you don’t trust the AI to answer you honestly?

chmod775 8mo ago
You're anthropomorphizing. LLMs can't lie nor can they tell the truth. These concepts just don't apply to them. They also cannot tell you what they were "thinking" when they wrote a piece of code. If you "ask" them what they were thinking, you just get a plausible response, not the "intention" that may or may not have existed in some abstract form in some layer when the system selected tokens*. That information is gone at that point and the LLM has no means to turn that information into something a human could understand anyways. They simply do not have what in a human might be called metacognition. For now. There's lots of ongoing experimental research in this direction though. Chances are that when you ask an LLM about their output, you'll get the response of either someone who now recognized an issue with their work, or the likeness of someone who believes they did great work and is now defending it. Obviously this is based on the work itself being fed back through the context window, which will inform the response, and thus it may not be entirely useless, but... this is all very far removed from what a conscious being might explain about their thoughts. The closest you can currently get to this is reading the "reasoning" tokens, though even those are just some selected system output that is then fed back to inform later output. There's nothing stopping the system from "reasoning" that it should say A, but then outputting B. Example: https://i.imgur.com/e8PX84Z.png https://i.imgur.com/e8PX84Z.png * One might say that the LLM itself always considers every possible token and assigns weights to them, so there wouldn't even be a single chain of thought in the first place. More like... every possible "thought" at the same time at varying intensities.
- pipes 8mo ago
  Or ask another model to tell you what the changes do.
  - chmod775 8mo ago
    Did you mean to reply to some other comment? I'm having trouble contextualizing your response - pardon the pun.
  - deleted 8mo ago
    [deleted]
  - cyclopeanutopia 8mo ago
    And you could first read the thing to which you are replying. Don't tell me it was too long.
    - seanmcdirmid 8mo ago
      It was too long, but thankfully AI can summarize so it’s no excuse anymore.
- seanmcdirmid 8mo ago
  I’m not anthropomorphizing. I’ve been in many situation where the AI wrote some code some way and I had to ask why, it told me why and then we moved on to better solutions as needed. Better if it just wrote the code and its reasoning was still in context, but even if it’s not, it can usually reverse engineer what it wrote well enough. Then it’s a conversation about whether there is a better clearer way to do it, the code improves. It sounds like you either have access to bad models or you are just imagining what it’s like to use an LLM in this way and haven’t actually tried asking it why it wrote something. The only judgement you need to make is the explanation makes sense or not, not some technical or theoretical argument about where the tokens in the explanation come from. You just ask questions until you can easily verify things for yourself. Also, pretending that the LLM is still just token predicting and isn’t bringing in a lot of extra context via RAG and using extra tokens for thinking to answer a query is just way out there.

tayo42 8mo ago

Your always reviewing code though. Either a team mates pr or maybe your own code in 3 months, or some legacy thing.

christophilus 8mo ago
Human code is still easier to review. Also, I program 80% of the time and review PRs 20% of the time. With AI, that becomes: I review 80% of the time, and write markdown and wait 20% of the time.