4 ms·

Working with LLMs is great for building communication skills. Communicating effectively is one of the hardest skills and it's baked into everything we do as hum

by apsurd 4mo ago

Working with LLMs is great for building communication skills. Communicating effectively is one of the hardest skills and it's baked into everything we do as humans. I'd say as a matter of principle: blame it on a communication failure on your end vs blaming the stupid LLM since you're the only one that can do anything about it. So I don't think it's a matter of form; whether the AI should or shouldn't act like a human. > Practically speaking, I probably just need to condition myself not to get caught in the illusion of speaking with a human. Though I’m not really thrilled about a future where I need to guard against the tools I use for my job.

rpcope1 4mo ago
That's been one of the gravest re-realizations I've noticed watching coworkers trying to pick up "agentic" coding: they often just break down into "just fix it" or "why is this broke". I've noticed that even though supposedly there's training or some sort of work done to make the agent work better with unclear or ambiguous grammar or bad structure, it feels like the quality changes palpably when you talk in clear well-structured English and provide at least a good background on the task. To me all of that feels natural, and I like writing and explaining anyways, but it's seemed like an almost insurmountable obstacle for some I've met (and I'm not even talking ESLs either). I strongly suspect those communication and writing skills will be a major factor in the bifurcation of haves and have nots as software "engineering" as we understand it continues to change.
- aa-jv 4mo ago
  Agreed, 100%. If you cannot formulate a specification, or describe a requirement - or indeed, if you cannot fathom the difference between a spec and a requirement, and why its needed to differentiate these from each other prior to doing a proper design and implementation - then you're going to carry your bad practice into the AI realm and that AI is going to be a force multiplier of your own bad practice. Because you will never know if the specs/reqs/design chosen by the AI are actually appropriate, unless you yourself review those specs/reqs/designs, AND the code produced by the AI to fulfill those specs/reqs/designs... AI makes the able software developer, more able. But it also makes the unable software developer, even more unable - with the risk of exceeding the AI-users limits on the Peter Principle scale.. in fact, AI will propel you to the middle of your own Peter Principle dilemma faster than you can type, probably. Communication and writing skills are essential, with or without AI. But reading skills are even more relevant when dealing with AI. Alas, so few people who choose to use AI, have the temerity to actually do the work - or else they wouldn't be rushing for the AI tool in the first place. Review, review, review. Always. Read the damn code, no matter who or what wrote it. Make sure it fulfills the specs and requirements its supposed to fulfill - and even more important make sure you, the reviewer, also understand the specs and requirements. And if you don't, fix that - don't ship it anyway, ffs!
  - rpcope1 4mo ago
    I am maybe positing something even stronger: say you had two prompts, both with the same information, one was written in the style of a good paper out of Nature or Science, one written in the style of a bad Twitter post or other kind of mess, even with the same information, I increasingly believe even for the top end models like Opus, the results are at least materially different if not grossly so. I really believe the stronger English input yields demonstrably better outcomes, even if the information contained inside is the same.
    - HarHarVeryFunny 4mo ago
      Of course - remember it is not trying to answer your question or do what you ask, just trying to do what it was trained to do which is generate the statistically most likely continuation of your input. Your input sets the tone for the type of response that is statistically likely to follow. Idiots tend to engage with other idiots, and experts tend to seek out experts to talk to .. this is what it has been trained on, and is how it is selecting the best (most likely) “response” (continuation) to your input.
      - ytoawwhra92 4mo ago
        For many of these tools your prompt is a small part of the initial input, and it shrinks as a proportion of the total as more information is added to the context. IME you can get great results from crappy prompts if the surrounding context is high quality, and you'll often get terrible results from great prompts if the surrounding context is low quality or non-existent. Same goes for the training corpus, I imagine. I think success with LLMs is dominated by giving them a well-structured workspace, high-quality reference material, and strict tools with which they can check their work. Then you only need great prompts when venturing into parts unknown. But for that stuff you may as well write it by hand and get the LLM to fill in the gaps IMO.
        HarHarVeryFunny 4mo ago
        True - and some may have been RLHF’d to give more rigorous/expert responses regardless of how the question/request is posed. Still, others responding here say that swearing at the model can be helpful (a signal to cut the BS I suppose), and an LLM wouldn’t be doing the job it was trained for if it’s response wasn’t highly attuned to the input
- carsareok 4mo ago
  Yes, I have definitely witnessed this as well. I think, I hope, this will be fixable to some degree, but at this moment I believe it's best to communicate in Queen's English and try to maintain the level of clarity of thought you expect of them in return. My pet theory is that actual real conversations they were trained on with bad grammar and spelling are in general relatively starved of proper reasoning. By talking to them in this fashion you activate their lowbrow patterns and while it may not be catastrophic I can't imagine it helps.
  - throw310822 4mo ago
    Also, quite simply, the output is a function of the requirements and the context. If you can't communicate clearly what the situation is and what you want, what do you expect the LLM to do, read your mind?
    - carsareok 4mo ago
      Oh yes, the part where actual crucial non-derivable information was left out certainly comes into play as well. I suspect many people underestimate the sheer magnitude of implicit cultural and organizational knowledge they and their colleagues carry with them. It's always part of their context in everything they do, so to speak and they expect the "bots" to carry this as well (without giving it to them).
pscanf 4mo ago
Author here. I definitely agree that communicating well is a prerequisite to getting decent results. On the other hand: 1. Even if you communicate perfectly, there's no guarantee that the LLM will "behave as instructed" and as you imagined it to. Indeed, the frustration often comes from the fact that you've said something as clear as day, yet the agent takes another path. 2. Part of the value of coding agents is exactly that you don't need to lay it all out perfectly for them. I mean, if I need to give the LLM every little implementation detail, I might as well write the code. Of course, I don't expect it to work off of "I want nice app make money", but I do expect some "intelligence" in figuring out the missing pieces.
- scotty79 4mo ago
  > Even if you communicate perfectly, there's no guarantee that the LLM will "behave as instructed" and as you imagined it to. Indeed, the frustration often comes from the fact that you've said something as clear as day, yet the agent takes another path. People forget. People misunderstand clear things. Teach yourself to not judge people for being human. You'll have easier time with AI. You are not gonna be angry at 5 year old because it occasionally can't follow your instructions. AI is a 5 year old that accidentally ate all the encyclopedias in the world and is super eager to help. Be a more charitable, generous, understanding person, even in the absence of actual people. Also try a stronger model. There is a difference. I have very good results with Codex but don't get fixated on any one, they are all "state of the art" or close but they are different and state of the art is moving ahead faster and faster.
  - watwut 4mo ago
    > Teach yourself to not judge people for being human. You'll have easier time with AI. LLM is not a human. This implication that OP or someone else is impatient against people when they get frustrated with effin machine is completely absurd. > AI is a 5 year old that accidentally ate all the encyclopedias in the world and is super eager to help LLM is not 5 years old kid. It is an expensive tool.
    - scotty79 4mo ago
watwut 4mo ago
LLM is a tool, it is not communication failure. This is like saying I should treat null pointer with workaround as a communication failure between me and the software.
randusername 4mo ago
More specifically it is about efficiently conveying outside context. The 4 horsemen of AI dismissal: 1. Slow typist 2. Terse communicator / ambiguous "it" "that" "this" 3. Assumes conversation partners share their reality and headspace 4. Mental blocks with delegation, even to competent humans

by apsurd 4mo ago

rpcope1 4mo ago

That's been one of the gravest re-realizations I've noticed watching coworkers trying to pick up "agentic" coding: they often just break down into "just fix it" or "why is this broke". I've noticed that even though supposedly there's training or some sort of work done to make the agent work better with unclear or ambiguous grammar or bad structure, it feels like the quality changes palpably when you talk in clear well-structured English and provide at least a good background on the task. To me all of that feels natural, and I like writing and explaining anyways, but it's seemed like an almost insurmountable obstacle for some I've met (and I'm not even talking ESLs either). I strongly suspect those communication and writing skills will be a major factor in the bifurcation of haves and have nots as software "engineering" as we understand it continues to change.

aa-jv 4mo ago
Agreed, 100%. If you cannot formulate a specification, or describe a requirement - or indeed, if you cannot fathom the difference between a spec and a requirement, and why its needed to differentiate these from each other prior to doing a proper design and implementation - then you're going to carry your bad practice into the AI realm and that AI is going to be a force multiplier of your own bad practice. Because you will never know if the specs/reqs/design chosen by the AI are actually appropriate, unless you yourself review those specs/reqs/designs, AND the code produced by the AI to fulfill those specs/reqs/designs... AI makes the able software developer, more able. But it also makes the unable software developer, even more unable - with the risk of exceeding the AI-users limits on the Peter Principle scale.. in fact, AI will propel you to the middle of your own Peter Principle dilemma faster than you can type, probably. Communication and writing skills are essential, with or without AI. But reading skills are even more relevant when dealing with AI. Alas, so few people who choose to use AI, have the temerity to actually do the work - or else they wouldn't be rushing for the AI tool in the first place. Review, review, review. Always. Read the damn code, no matter who or what wrote it. Make sure it fulfills the specs and requirements its supposed to fulfill - and even more important make sure you, the reviewer, also understand the specs and requirements. And if you don't, fix that - don't ship it anyway, ffs!
- rpcope1 4mo ago
  I am maybe positing something even stronger: say you had two prompts, both with the same information, one was written in the style of a good paper out of Nature or Science, one written in the style of a bad Twitter post or other kind of mess, even with the same information, I increasingly believe even for the top end models like Opus, the results are at least materially different if not grossly so. I really believe the stronger English input yields demonstrably better outcomes, even if the information contained inside is the same.
  - HarHarVeryFunny 4mo ago
    Of course - remember it is not trying to answer your question or do what you ask, just trying to do what it was trained to do which is generate the statistically most likely continuation of your input. Your input sets the tone for the type of response that is statistically likely to follow. Idiots tend to engage with other idiots, and experts tend to seek out experts to talk to .. this is what it has been trained on, and is how it is selecting the best (most likely) “response” (continuation) to your input.
    - ytoawwhra92 4mo ago
      For many of these tools your prompt is a small part of the initial input, and it shrinks as a proportion of the total as more information is added to the context. IME you can get great results from crappy prompts if the surrounding context is high quality, and you'll often get terrible results from great prompts if the surrounding context is low quality or non-existent. Same goes for the training corpus, I imagine. I think success with LLMs is dominated by giving them a well-structured workspace, high-quality reference material, and strict tools with which they can check their work. Then you only need great prompts when venturing into parts unknown. But for that stuff you may as well write it by hand and get the LLM to fill in the gaps IMO.
      - HarHarVeryFunny 4mo ago
        True - and some may have been RLHF’d to give more rigorous/expert responses regardless of how the question/request is posed. Still, others responding here say that swearing at the model can be helpful (a signal to cut the BS I suppose), and an LLM wouldn’t be doing the job it was trained for if it’s response wasn’t highly attuned to the input
carsareok 4mo ago
Yes, I have definitely witnessed this as well. I think, I hope, this will be fixable to some degree, but at this moment I believe it's best to communicate in Queen's English and try to maintain the level of clarity of thought you expect of them in return. My pet theory is that actual real conversations they were trained on with bad grammar and spelling are in general relatively starved of proper reasoning. By talking to them in this fashion you activate their lowbrow patterns and while it may not be catastrophic I can't imagine it helps.
- throw310822 4mo ago
  Also, quite simply, the output is a function of the requirements and the context. If you can't communicate clearly what the situation is and what you want, what do you expect the LLM to do, read your mind?
  - carsareok 4mo ago
    Oh yes, the part where actual crucial non-derivable information was left out certainly comes into play as well. I suspect many people underestimate the sheer magnitude of implicit cultural and organizational knowledge they and their colleagues carry with them. It's always part of their context in everything they do, so to speak and they expect the "bots" to carry this as well (without giving it to them).

pscanf 4mo ago

Author here. I definitely agree that communicating well is a prerequisite to getting decent results. On the other hand: 1. Even if you communicate perfectly, there's no guarantee that the LLM will "behave as instructed" and as you imagined it to. Indeed, the frustration often comes from the fact that you've said something as clear as day, yet the agent takes another path. 2. Part of the value of coding agents is exactly that you don't need to lay it all out perfectly for them. I mean, if I need to give the LLM every little implementation detail, I might as well write the code. Of course, I don't expect it to work off of "I want nice app make money", but I do expect some "intelligence" in figuring out the missing pieces.

scotty79 4mo ago
> Even if you communicate perfectly, there's no guarantee that the LLM will "behave as instructed" and as you imagined it to. Indeed, the frustration often comes from the fact that you've said something as clear as day, yet the agent takes another path. People forget. People misunderstand clear things. Teach yourself to not judge people for being human. You'll have easier time with AI. You are not gonna be angry at 5 year old because it occasionally can't follow your instructions. AI is a 5 year old that accidentally ate all the encyclopedias in the world and is super eager to help. Be a more charitable, generous, understanding person, even in the absence of actual people. Also try a stronger model. There is a difference. I have very good results with Codex but don't get fixated on any one, they are all "state of the art" or close but they are different and state of the art is moving ahead faster and faster.
- watwut 4mo ago
  > Teach yourself to not judge people for being human. You'll have easier time with AI. LLM is not a human. This implication that OP or someone else is impatient against people when they get frustrated with effin machine is completely absurd. > AI is a 5 year old that accidentally ate all the encyclopedias in the world and is super eager to help LLM is not 5 years old kid. It is an expensive tool.
  - scotty79 4mo ago

watwut 4mo ago

LLM is a tool, it is not communication failure. This is like saying I should treat null pointer with workaround as a communication failure between me and the software.

randusername 4mo ago

More specifically it is about efficiently conveying outside context. The 4 horsemen of AI dismissal: 1. Slow typist 2. Terse communicator / ambiguous "it" "that" "this" 3. Assumes conversation partners share their reality and headspace 4. Mental blocks with delegation, even to competent humans