Writing customer/company-wide emails is a good example. “Make this sound better: we’re aware of the outage at Site A, we are working as quick as possible to get things back online”
Dumbing down technical information “word this so a non-technical person can understand: our DHCP scope filled up and there were no more addresses available for Site A, which caused the temporary outage for some users”
Another is feeding it an article and asking for a summary, https://hackingne.ws/ does that for its Bsky posts.
Coding is another good example, “write me a Python script that moves all files in /mydir to /newdir”
Asking for it to summarize a theory or protocol, “explain to me why RIP was replaced with RIPv2, and what problems people have had since with RIPv2”
Make this sound better: we’re aware of the outage at Site A, we are working as quick as possible to get things back online
How does this work in practice? I suspect you’re just going to get an email that takes longer for everyone to read, and doesn’t give any more information (or worse, gives incorrect information). Your prompt seems like what you should be sending in the email.
If the model (or context?) was good enough to actually add useful, accurate information, then maybe that would be different.
I think we’ll get to the point really quickly where a nice concise message like in your prompt will be appreciated more than the bloated, normalised version, which people will find insulting.
Yeah, normally my “Make this sound better” or “summarize this for me” is a longer wall of text that I want to simplify, I was trying to keep my examples short. Talking to non-technical people about a technical issue is not the easiest for me, AI has helped me dumb it down when sending an email, and helps correct my shitty grammar at times.
As for accuracy, you review what it gives you, you don’t just copy and send it without review. Also you will have to tweak some pieces that it gives out where it doesn’t make the most sense, such as if it uses wording you wouldn’t typically use. It is fairly accurate though in my use-cases.
Hallucinations are a thing, so validating what it spits out is definitely needed.
Another example: if you feel your email is too stern or gives the wrong tone, I’ve used it for that as well. “Make this sound more relaxed: well maybe if you didn’t turn off the fucking server we wouldn’t of had this outage!” (Just a silly example)
I think these are actually valid examples, albeit ones that come with a really big caveat; you’re using AI in place of a skill that you really should be learning for yourself. As an autistic IT person, I get the struggle of communicating with non-technical and neurotypical people, especially clients who you have to be extra careful with. But the reality is, you can’t always do all your communication by email. If you always rely on the AI to correct your tone or simplify your language, you’re choosing not to build an essential skill that is every bit as important to doing your job well as it is to know how to correctly configure an ACL on a Cisco managed switch.
That said, I can also see how relying on the AI at first can be a helpful learning tool as you build those skills. There’s certainly an argument that by using tools, but paying attention to the output of those tools, you build those skills for yourself. Learning by example works. I think used in that way, there’s potentially real value there.
Which is kind of the broader story with Gen AI overall. It’s not that it can never be useful; it’s that, at best, it can only ever aspire to “useful.” No one, yet, has demonstrated any ability to make AI “essential” and the idea that we should be investing hundreds of billions of dollars into a technology that is, on its best days, mildly useful, is sheer fucking lunacy.
Noted, I’ll be giving that a proper read after work. Thank you.
Edit to add: Yeah, that pretty much mirrors my own experiences of using AI as a coding aid. Even when I was learning a new language, I found that my comprehension of the material very quickly outstripped whatever ChatGPT could provide. I’d much rather understand what I’m building because I built it myself. A lot of the time, when you use a solution someone else provided you don’t find out until much later how badly that solution held you back because it wasn’t actually the best way to tackle the problem.
The dumbed down text is basically as long as the prompt. Plus you have to double check it to make sure it didn’t have outrage instead of outage just like if you wrote it yourself.
How do you know the answer on why RIP was replaced with RIPv2 is accurate and not just a load of bullshit like putting glue on pizza?
If the amount of time it takes to create the prompt is the same as it would have taken to write the dumbed down text, then the only time you saved was not learning how to write dumbed down text. Plus you need to know what dumbed down text should look like to know if the output is dumbed down but still accurate.
My experience has been very different, I do have to sometimes add to what it summarized though. The Bsky account mentioned is a good example, most of the posts are very well summarized, but every now and then there will be one that isn’t as accurate.
Writing customer/company-wide emails is a good example. “Make this sound better: we’re aware of the outage at Site A, we are working as quick as possible to get things back online”
Dumbing down technical information “word this so a non-technical person can understand: our DHCP scope filled up and there were no more addresses available for Site A, which caused the temporary outage for some users”
Another is feeding it an article and asking for a summary, https://hackingne.ws/ does that for its Bsky posts.
Coding is another good example, “write me a Python script that moves all files in /mydir to /newdir”
Asking for it to summarize a theory or protocol, “explain to me why RIP was replaced with RIPv2, and what problems people have had since with RIPv2”
How does this work in practice? I suspect you’re just going to get an email that takes longer for everyone to read, and doesn’t give any more information (or worse, gives incorrect information). Your prompt seems like what you should be sending in the email.
If the model (or context?) was good enough to actually add useful, accurate information, then maybe that would be different.
I think we’ll get to the point really quickly where a nice concise message like in your prompt will be appreciated more than the bloated, normalised version, which people will find insulting.
Yes, people are using it as the least efficient communication protocol ever.
One side asks an LLM to expand a summary into a fluff filled email, and the other side asks an LLM to reduce the long email to a summary.
Yeah, normally my “Make this sound better” or “summarize this for me” is a longer wall of text that I want to simplify, I was trying to keep my examples short. Talking to non-technical people about a technical issue is not the easiest for me, AI has helped me dumb it down when sending an email, and helps correct my shitty grammar at times.
As for accuracy, you review what it gives you, you don’t just copy and send it without review. Also you will have to tweak some pieces that it gives out where it doesn’t make the most sense, such as if it uses wording you wouldn’t typically use. It is fairly accurate though in my use-cases.
Hallucinations are a thing, so validating what it spits out is definitely needed.
Another example: if you feel your email is too stern or gives the wrong tone, I’ve used it for that as well. “Make this sound more relaxed: well maybe if you didn’t turn off the fucking server we wouldn’t of had this outage!” (Just a silly example)
I think these are actually valid examples, albeit ones that come with a really big caveat; you’re using AI in place of a skill that you really should be learning for yourself. As an autistic IT person, I get the struggle of communicating with non-technical and neurotypical people, especially clients who you have to be extra careful with. But the reality is, you can’t always do all your communication by email. If you always rely on the AI to correct your tone or simplify your language, you’re choosing not to build an essential skill that is every bit as important to doing your job well as it is to know how to correctly configure an ACL on a Cisco managed switch.
That said, I can also see how relying on the AI at first can be a helpful learning tool as you build those skills. There’s certainly an argument that by using tools, but paying attention to the output of those tools, you build those skills for yourself. Learning by example works. I think used in that way, there’s potentially real value there.
Which is kind of the broader story with Gen AI overall. It’s not that it can never be useful; it’s that, at best, it can only ever aspire to “useful.” No one, yet, has demonstrated any ability to make AI “essential” and the idea that we should be investing hundreds of billions of dollars into a technology that is, on its best days, mildly useful, is sheer fucking lunacy.
I have a blog for you
Noted, I’ll be giving that a proper read after work. Thank you.
Edit to add: Yeah, that pretty much mirrors my own experiences of using AI as a coding aid. Even when I was learning a new language, I found that my comprehension of the material very quickly outstripped whatever ChatGPT could provide. I’d much rather understand what I’m building because I built it myself. A lot of the time, when you use a solution someone else provided you don’t find out until much later how badly that solution held you back because it wasn’t actually the best way to tackle the problem.
The dumbed down text is basically as long as the prompt. Plus you have to double check it to make sure it didn’t have outrage instead of outage just like if you wrote it yourself.
How do you know the answer on why RIP was replaced with RIPv2 is accurate and not just a load of bullshit like putting glue on pizza?
Are you really saving time?
Dumbed down doesn’t mean shorter.
If the amount of time it takes to create the prompt is the same as it would have taken to write the dumbed down text, then the only time you saved was not learning how to write dumbed down text. Plus you need to know what dumbed down text should look like to know if the output is dumbed down but still accurate.
it’s not good for summaries. often gets important bits wrong, like embedded instructions that can’t be summarized.
My experience has been very different, I do have to sometimes add to what it summarized though. The Bsky account mentioned is a good example, most of the posts are very well summarized, but every now and then there will be one that isn’t as accurate.