It also isn’t telepathic, so the only thing it has to go on when determining “what you want” is what you tell it you want.
I often see people gripe about how ChatGPT’s essay writing style is mediocre and always sounds the same, for example. But that’s what you get when you just tell ChatGPT “write me an essay about X.” It doesn’t know what kind of essay you want unless you tell it. You have to give it context and direction to get good results.
Not disagreeing with you at all, you made a pretty good point. But when engineering the prompt takes 80% of the effort that just writing the essay (or code for that matter) would take, I think most people would rather write it themselves.
Sure, in those situations. I find that it doesn’t take that much effort to write a prompt that gets me something useful in most situations, though. You just need to make some effort. A lot of people don’t put in any effort, get a bad result, and conclude “this tech is useless.”
We are all annoyed at clients for not saying what they actually want in a Scope of Works, yet we do the same to LLM thinking it will fill in the blanks how we want it filled in.
You communicate with co-workers using natural languages but that doesn’t make co-workers useless. You just have to account for the strengths and weaknesses of that mechanism in your workflow.
I treat AI the same way I’ve always treated Google: WITH ABSOLUTE DISDAIN Using them as a shove in the right direction and for research purposes to supplement research already being done. ChatGPT for instance is actually pretty decent at figuring out vaguely defined things if worked through. Is it perfect? Hell no. It can help narrow down the options though.
I’m pretty anti-AI but even I’ll cop to this one. ChatGPT is good at figuring out what you’re trying to describe. Know you need a particular networking concept? Describe it a bit to ChatGPT and ask for some concepts that are similar, and the thing you’re looking for will probably be in the list.
Looking for a particular library that you assume must exist even though you’ve never seen it? ChatGPT can give you that.
You’re on your own after that, but it can actually save you a bit of research time.
The problem is this: it’s sure it has the answer 100% of the time, but about 30% of the time it gives you a list of nothing but wrong answers and you can go off in the wrong direction as a result.
People really should remember: generative AI makes things things that look like what you want.
Now, usually that overlaps a lot with what you actually want, but not nearly always, and especially not when details matter.
It also isn’t telepathic, so the only thing it has to go on when determining “what you want” is what you tell it you want.
I often see people gripe about how ChatGPT’s essay writing style is mediocre and always sounds the same, for example. But that’s what you get when you just tell ChatGPT “write me an essay about X.” It doesn’t know what kind of essay you want unless you tell it. You have to give it context and direction to get good results.
Not disagreeing with you at all, you made a pretty good point. But when engineering the prompt takes 80% of the effort that just writing the essay (or code for that matter) would take, I think most people would rather write it themselves.
Sure, in those situations. I find that it doesn’t take that much effort to write a prompt that gets me something useful in most situations, though. You just need to make some effort. A lot of people don’t put in any effort, get a bad result, and conclude “this tech is useless.”
We are all annoyed at clients for not saying what they actually want in a Scope of Works, yet we do the same to LLM thinking it will fill in the blanks how we want it filled in.
Yet that’s usually enough when taking to another developer.
The problem is that we have this unambiguous language that is understood by human and a computer to tell computer exactly what we want to do.
With LLM we instead opt to use a natural language that is imprecise and full of ambiguity to do the same.
You communicate with co-workers using natural languages but that doesn’t make co-workers useless. You just have to account for the strengths and weaknesses of that mechanism in your workflow.
I treat AI the same way I’ve always treated Google:
WITH ABSOLUTE DISDAINUsing them as a shove in the right direction and for research purposes to supplement research already being done. ChatGPT for instance is actually pretty decent at figuring out vaguely defined things if worked through. Is it perfect? Hell no. It can help narrow down the options though.I’m pretty anti-AI but even I’ll cop to this one. ChatGPT is good at figuring out what you’re trying to describe. Know you need a particular networking concept? Describe it a bit to ChatGPT and ask for some concepts that are similar, and the thing you’re looking for will probably be in the list.
Looking for a particular library that you assume must exist even though you’ve never seen it? ChatGPT can give you that.
You’re on your own after that, but it can actually save you a bit of research time.
The problem is this: it’s sure it has the answer 100% of the time, but about 30% of the time it gives you a list of nothing but wrong answers and you can go off in the wrong direction as a result.
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