11 ms·

I recently had this happen from a senior engineer. What's really frustrating is I TOLD them the issues and how to fix it. Instead of listening to what I told

I recently had this happen from a senior engineer. What's really frustrating is I TOLD them the issues and how to fix it. Instead of listening to what I told them, they plugged it into GPT and responded with "Oh, interesting this is what GPT says" (Which, spoiler, was similar but lacking from what I'd said). Meaning, instead of listening to a real-life expert in the company telling them how to handle the problem they ignored my advice and instead dumped the garbage from GPT. I really fear that a number of engineers are going to us GPT to avoid thinking. They view it as a shortcut to problem solve and it isn't.

delusional 2y ago
Those people weren't engineers to start with.
- layer8 2y ago
  Software engineers rarely are. I’m saying this tongue in cheek, but there’s some truth to it.
  - throwanem 2y ago
    There is much truth. Railway engineers 'rarely were' too, once upon a time, and for in my view essentially the same reasons.
colechristensen 2y ago
If I had a dollar for every time I told someone how to fix something and they did something else... Let's just say not listening to someone and then complaining that doing something else didn't work isn't exactly new.
colechristensen 2y ago
>They view it as a shortcut to problem solve and it isn't Oh but it is, used wisely. One: it's a replacement for googling a problem and much faster. Instead of spending half an hour or half a day digging through bug reports, forum posts, and stack overflow for the solution to a problem. LLMs are a lot faster, occasionally correct, and very often at least rather close. Two: it's a replacement for learning how to do something I don't want to learn how to do. Case Study: I have to create a decent-enough looking static error page for a website. I could do an awful job with my existing knowledge, I could spend half a day relearning and tweaking CSS, elements, etc. etc. or I could ask an LLM to do it and then tweak the results. Five minutes for "good enough" and it really is. LLMs are not a replacement for real understanding, for digging into a codebase to really get to the core of a problem, or for becoming an expert in something, but in many cases I do not want to, and moreover it is a poor use of my time. Plenty of things are not my core competence or anywhere near the goals I'm trying to achieve. I just need a quick solution for a topic I'm not interested in.
- ijidak 2y ago
  This exactly! There are so many things that a human worker or coder has to do in a day and a lot of those things are non-core. If someone is trying to be an expert on every minor task that comes across their desk, they were never doing it right. An error page is a great example. There is functionality that sets a company apart and then there are things that look the same across all products. Error pages are not core IP. At almost any company, I don't want my $200,000-300,000 a year developer mastering the HTML and CSS of an error page.
- vuserfcase 2y ago
  >Oh but it is, used wisely. Sufficiently advanced orange juice extractor is the solution to any problem. Doesen't necessarily mean you should build the sufficient part. >One: it's a replacement for googling a problem and much faster This is more to do with the problem that google results have gone downhill very rapidly. It used to be you could find what you were looking for very fast and solve a problem. >I could ask an LLM to do it and then tweak the results. Five minutes for "good enough" and it really is. When the cost of failures is low, a hackjob can be economical, like a generated picture for entertainment or a static error page. Miscreating a support for a bridge it is not very economical
throwanem 2y ago
You should ask yourself why this organization wants engineering advice from a chatbot more than from you. I doubt the reason has to do with your qualities as an engineer, which must be basically sound. Otherwise why bother to launder the product of your judgment, as you described here someone doing?
silversmith 2y ago
I often do this - ask a LLM for an answer when I already have it from an expert. I do it to evaluate the ability of the LLM. Usually not in the presence of said expert tho.
- namaria 2y ago
  Just using LLMs on the (few) things I have specialist knowledge of it's clear they are extremely limited. I get absurdly basic mistakes and I am very wary of even reading LLM output about topics I don't command. It's easy to get stuck on dead ends reasoning wise even by getting noisy input.
_ea1k 2y ago
I wonder if this is an indication that they didn't really understand what you said to begin with.
tharant 2y ago
Is it possible that what happened was an impedance mismatch between you and the engineer such that they couldn’t grok what you told them but ChatGPT was able to describe it in a manner they could understand? Real-life experts (myself included, though I don’t claim to be an expert in much) sometimes have difficulty explaining domain-specific concepts to other folks; it’s not a flaw in anyone, folks just have different ways of assembling mental models.
- kevmo314 2y ago
  Whenever someone has done that to me, it's clear they didn't read the ChatGPT output either and were sending it to me as some sort of "look someone else thinks you're wrong".
  - tharant 2y ago
    Again, is it possible you and the other party have (perhaps significantly) different mental models of the domain—or maybe different perspectives of the issues involved? I get that folks can be contrarian (sadly, contrariness is probably my defining trait) but it seems unlikely that someone would argue that you’re wrong by using output they didn’t read. I see impedance mismatches regularly yet folks seem often to assume laziness/apathy/stupidity/pride is the reason for the mismatch. Best advice I ever received is “Assume folks are acting rationally, with good intention, and with a willingness to understand others.” — which for some reason, in my contrarian mind, fits oddly nicely with Hanlon’s razor but I tend to make weird connections like that.
    - kevmo314 2y ago
      > is it possible you and the other party have (perhaps significantly) different mental models of the domain—or maybe different perspectives of the issues involved? Yes, however typically if that's the case they will respond with some variant of "ChatGPT mentioned xyz so I started poking in that direction, does that make sense?" There is a markedly different response when people are using ChatGPT to try to understand better and that I have no issue with. I get what you're suggesting but I don't think people are being malicious, it's more that the discussion has gotten too deep and they're exhausted so they'd rather opt out. In some cases yes it does mean the discussion could've been simplified, but sometimes when it's a pretty deep, technical reason it's hard to avoid. A concrete example is we had to figure out a bug in some assembly code once and we were looking at a specific instruction. I didn't believe that instruction was wrong and I pointed at the docs suggesting it lined up with what we were observing it doing. Someone responded with "I asked ChatGPT and here's what it said: ..." without even a subsequent opinion on the output of ChatGPT. In fact, reading the output it basically restated what I said, but said engineer used that as justification to rewrite the instruction to something else. And at that point I was like y'know what, I just don't care enough. Unsurprisingly, it didn't work, and the bug never got fixed because I lost interest in continuing the discussion too. I think what you're describing does happen in good faith, but I think people also use the wall of text that ChatGPT produces as an indirect way to say "I don't care about your opinion on this matter anymore."
tharant 2y ago
> I really fear that a number of engineers are going to us GPT to avoid thinking. They view it as a shortcut to problem solve and it isn't. How is this sentiment not different from my grandfather’s sentiment that calculators and computers (and probably his grandfather’s view of industrialization) are a shortcut to avoid work? From my perspective most tools are used as a shortcut to avoid work; that’s kinda the while point—to give us room to think about/work on other stuff.
- stevage 2y ago
  Did you grandpa think that calculators made engineers worse at their jobs?
  - tharant 2y ago
    I don’t know for certain (he’s no longer around) but I suspect he did. The prevalence of folks who nowadays believe that Gen-AI makes everything worse suggests to me that not much has changed since his time. I get it; I’m not an AI evangelist and I get frustrated with the slop too; Gen-AI (and many of the tools we’ve enjoyed over the past few millennia) was/is lauded as “The” singular tool that makes everything better; no tool can fulfill that role yet we always try to shoehorn our problems into a shape that fits the tool. We just need to use the correct tools for the job; in my mind, the only problem right now is that we have a really capable tool and have identified some really valuable use-cases for that tool yet we also keep trying to use it for (what I believe are, given current capabilities) use-cases that don’t fit the tool. We’ll figure it out but, in the meantime, while I don’t like to generalize that a tech or its use-cases are objectively good/bad, I do tend to have an optimistic outlook for most tech—Gen-AI included.
- parliament32 2y ago
  Because calculators aren't confidently wrong the majority of the time.
  - tharant 2y ago
    In my experience, and for use-cases that are carefully considered, language models are not confidently wrong a majority of the time. The trick is understanding the tool and using it appropriately—thus the “carefully considered” approach to identifying use-cases that can provide value.

delusional 2y ago

Those people weren't engineers to start with.

layer8 2y ago
Software engineers rarely are. I’m saying this tongue in cheek, but there’s some truth to it.
- throwanem 2y ago
  There is much truth. Railway engineers 'rarely were' too, once upon a time, and for in my view essentially the same reasons.

colechristensen 2y ago

If I had a dollar for every time I told someone how to fix something and they did something else... Let's just say not listening to someone and then complaining that doing something else didn't work isn't exactly new.

colechristensen 2y ago

>They view it as a shortcut to problem solve and it isn't Oh but it is, used wisely. One: it's a replacement for googling a problem and much faster. Instead of spending half an hour or half a day digging through bug reports, forum posts, and stack overflow for the solution to a problem. LLMs are a lot faster, occasionally correct, and very often at least rather close. Two: it's a replacement for learning how to do something I don't want to learn how to do. Case Study: I have to create a decent-enough looking static error page for a website. I could do an awful job with my existing knowledge, I could spend half a day relearning and tweaking CSS, elements, etc. etc. or I could ask an LLM to do it and then tweak the results. Five minutes for "good enough" and it really is. LLMs are not a replacement for real understanding, for digging into a codebase to really get to the core of a problem, or for becoming an expert in something, but in many cases I do not want to, and moreover it is a poor use of my time. Plenty of things are not my core competence or anywhere near the goals I'm trying to achieve. I just need a quick solution for a topic I'm not interested in.

ijidak 2y ago
This exactly! There are so many things that a human worker or coder has to do in a day and a lot of those things are non-core. If someone is trying to be an expert on every minor task that comes across their desk, they were never doing it right. An error page is a great example. There is functionality that sets a company apart and then there are things that look the same across all products. Error pages are not core IP. At almost any company, I don't want my $200,000-300,000 a year developer mastering the HTML and CSS of an error page.
vuserfcase 2y ago
>Oh but it is, used wisely. Sufficiently advanced orange juice extractor is the solution to any problem. Doesen't necessarily mean you should build the sufficient part. >One: it's a replacement for googling a problem and much faster This is more to do with the problem that google results have gone downhill very rapidly. It used to be you could find what you were looking for very fast and solve a problem. >I could ask an LLM to do it and then tweak the results. Five minutes for "good enough" and it really is. When the cost of failures is low, a hackjob can be economical, like a generated picture for entertainment or a static error page. Miscreating a support for a bridge it is not very economical

throwanem 2y ago

You should ask yourself why this organization wants engineering advice from a chatbot more than from you. I doubt the reason has to do with your qualities as an engineer, which must be basically sound. Otherwise why bother to launder the product of your judgment, as you described here someone doing?

silversmith 2y ago

I often do this - ask a LLM for an answer when I already have it from an expert. I do it to evaluate the ability of the LLM. Usually not in the presence of said expert tho.

namaria 2y ago
Just using LLMs on the (few) things I have specialist knowledge of it's clear they are extremely limited. I get absurdly basic mistakes and I am very wary of even reading LLM output about topics I don't command. It's easy to get stuck on dead ends reasoning wise even by getting noisy input.

_ea1k 2y ago

I wonder if this is an indication that they didn't really understand what you said to begin with.

tharant 2y ago

Is it possible that what happened was an impedance mismatch between you and the engineer such that they couldn’t grok what you told them but ChatGPT was able to describe it in a manner they could understand? Real-life experts (myself included, though I don’t claim to be an expert in much) sometimes have difficulty explaining domain-specific concepts to other folks; it’s not a flaw in anyone, folks just have different ways of assembling mental models.

kevmo314 2y ago
Whenever someone has done that to me, it's clear they didn't read the ChatGPT output either and were sending it to me as some sort of "look someone else thinks you're wrong".
- tharant 2y ago
  Again, is it possible you and the other party have (perhaps significantly) different mental models of the domain—or maybe different perspectives of the issues involved? I get that folks can be contrarian (sadly, contrariness is probably my defining trait) but it seems unlikely that someone would argue that you’re wrong by using output they didn’t read. I see impedance mismatches regularly yet folks seem often to assume laziness/apathy/stupidity/pride is the reason for the mismatch. Best advice I ever received is “Assume folks are acting rationally, with good intention, and with a willingness to understand others.” — which for some reason, in my contrarian mind, fits oddly nicely with Hanlon’s razor but I tend to make weird connections like that.
  - kevmo314 2y ago
    > is it possible you and the other party have (perhaps significantly) different mental models of the domain—or maybe different perspectives of the issues involved? Yes, however typically if that's the case they will respond with some variant of "ChatGPT mentioned xyz so I started poking in that direction, does that make sense?" There is a markedly different response when people are using ChatGPT to try to understand better and that I have no issue with. I get what you're suggesting but I don't think people are being malicious, it's more that the discussion has gotten too deep and they're exhausted so they'd rather opt out. In some cases yes it does mean the discussion could've been simplified, but sometimes when it's a pretty deep, technical reason it's hard to avoid. A concrete example is we had to figure out a bug in some assembly code once and we were looking at a specific instruction. I didn't believe that instruction was wrong and I pointed at the docs suggesting it lined up with what we were observing it doing. Someone responded with "I asked ChatGPT and here's what it said: ..." without even a subsequent opinion on the output of ChatGPT. In fact, reading the output it basically restated what I said, but said engineer used that as justification to rewrite the instruction to something else. And at that point I was like y'know what, I just don't care enough. Unsurprisingly, it didn't work, and the bug never got fixed because I lost interest in continuing the discussion too. I think what you're describing does happen in good faith, but I think people also use the wall of text that ChatGPT produces as an indirect way to say "I don't care about your opinion on this matter anymore."

tharant 2y ago

> I really fear that a number of engineers are going to us GPT to avoid thinking. They view it as a shortcut to problem solve and it isn't. How is this sentiment not different from my grandfather’s sentiment that calculators and computers (and probably his grandfather’s view of industrialization) are a shortcut to avoid work? From my perspective most tools are used as a shortcut to avoid work; that’s kinda the while point—to give us room to think about/work on other stuff.

stevage 2y ago
Did you grandpa think that calculators made engineers worse at their jobs?
- tharant 2y ago
  I don’t know for certain (he’s no longer around) but I suspect he did. The prevalence of folks who nowadays believe that Gen-AI makes everything worse suggests to me that not much has changed since his time. I get it; I’m not an AI evangelist and I get frustrated with the slop too; Gen-AI (and many of the tools we’ve enjoyed over the past few millennia) was/is lauded as “The” singular tool that makes everything better; no tool can fulfill that role yet we always try to shoehorn our problems into a shape that fits the tool. We just need to use the correct tools for the job; in my mind, the only problem right now is that we have a really capable tool and have identified some really valuable use-cases for that tool yet we also keep trying to use it for (what I believe are, given current capabilities) use-cases that don’t fit the tool. We’ll figure it out but, in the meantime, while I don’t like to generalize that a tech or its use-cases are objectively good/bad, I do tend to have an optimistic outlook for most tech—Gen-AI included.
parliament32 2y ago
Because calculators aren't confidently wrong the majority of the time.
- tharant 2y ago
  In my experience, and for use-cases that are carefully considered, language models are not confidently wrong a majority of the time. The trick is understanding the tool and using it appropriately—thus the “carefully considered” approach to identifying use-cases that can provide value.