I’m not a huge fan of Amazon, but last time I had an issue with a parcel it was sorted out insanely fast by the AI assistant on the website.
Within literally 2 minutes I’d had a refund confirmed. No waiting for people to eventually pick up the phone after 40 minutes. No misunderstanding or annoying questions. The moment I pressed send on my message it instantly started formulating a reply.
The truncated version went:
“Hey I meant to get [x] delivery, but it hasn’t arrived. Can I get a refund?”
“Sure, your money will go back into [y] account in a few days. If the parcel turns up in the meantime, you can send it back by dropping it off at [z]”
How is a chatbot here better, faster, or more accurate than just a “return this” button on a web page? Chat bots like that take 10x the programming effort and actively make the user experience worse.
But that nuance is probably limited to a paragraph or two of text. There’s nothing the chatbot knows about the returns process at a specific company that isn’t contained in that paragraph. The question is just whether that paragraph is shown directly to the user, or if it’s filtered through an LLM first. The only thing I can think of is that chatbot might be able to rephrase things for confused users and help stop users from ignoring the instructions and going straight to human support.
That has nothing to do with AI and is strictly a return policy matter. You can get a return in less than 2 minutes by speaking to a human at Home Depot.
Businesses choose to either prioritize customer experience, or not.
There’s a big claim from Klarna - that I am not aware has been independently verified – that customers prefer their bot.
The cynic might say they were probably undertraining a skeleton crew of underpaid support reps. More optimistically, perhaps so many support inquiries are so simple that responding to them with a technology that can type a million words per minute should obviously be likely to increase customer satisfaction.
Personally, I’m happy with environmentally-acceptable and efficient technologies that respect consumers… assuming they are deployed in a world with robust social safety nets like universal basic income. Heh
You can just go to the order and click like 2 buttons. Chat is for when a situation is abnormal, and I promise you their bot doesn’t know how to address anything like that.
We can! We also know how to use web search, read an FAQ, interpret posted policies…
Some folks can’t find buttons under “My Account” but can find the chat box in the corner.
Also I suspect traditionally, you’ve been able to protect features from [ab]use by making them accessible to agents. Someone who would click a “request refund” button may not be willing to ask for a refund. I wonder how this will change as chatbots are popularized.
Do you feel like elaborating any? I’d love to find more uses. So far I’ve mostly found it useful in areas where I’m very unfamiliar. Like I do very little web front end, so when I need to, the option paralysis is gnarly. I’ve found things like Perplexity helpful to allow me to select an approach and get moving quickly. I can spend hours agonizing over those kinds of decisions otherwise, and it’s really poorly spent time.
I’ve also found it useful when trying to answer questions about best practices or comparing approaches. It sorta does the reading and summarizes the points (with links to source material), pretty perfect use case.
So both of those are essentially “interactive text summarization” use cases - my third is as a syntax helper, again in things I don’t work with often. If I’m having a brain fart and just can’t quite remember the ternary operator syntax in that one language I never use…etc. That one’s a bit less impactful but can still be faster than manually inspecting docs, especially if the docs are bad or hard to use.
With that said I use these things less than once a week on average. Possible that’s just down to my own pre-existing habits more than anything else though.
An example I did today was adjusting the existing email functionality of the application I am working on to use handlebars templates. I was able to reformat the existing html stored as variables into the templates, then adjust their helper functions used to distribute the emails to work with handlebars rather than the previous system all in one fell swoop. I could have done it by hand, but it is repetitive work.
I also use it a lot when troubleshooting issues, such as suggesting how to solve error messages when I am having trouble understanding them. Just pasing the error into the chat has gotten me unstuck too many times to count.
It can also be super helpful when trying to get different versions of the packages installed in a code base to line up correctly, which can be absolutely brutal for me when switching between multiple projects.
Asking specific little questions that may take up the of a coworker or the Sr dev lets me understand the specifics of what I am looking at super quickly without wasting peoples time. I work mainly with existing code, so it is really helpful for breaking down other peoples junk if I am having trouble following.
Cool thanks! I haven’t tried it for troubleshooting, I’ll give that a go when I next need it.
Are you using one integrated into your IDE? Or just standalone in a web browser? That’s probably what I ought to try next (the IDE end of things). I saw an acquaintance using PyCharm’s integrated assistant to auto gen commit messages, that looked cool. Not exactly game changing of course.
Are you finding that the assistance it provides has gotten worse over time? When I first started using it, it was quite helpful the majority of the time. In truth, it’s still pretty decent with autocomplete, just less consistently good than before. However the chat help has truly gone into decline. The amount of unfounded statements it returns is terrible.
And the latest issue is that I’ve started getting responses where it starts to show me an answer, but then hides the response and gives me an error that the response was filtered by Responsible AI.
So how “intelligent” do you think the amazon returns bot is? As smart as a choose-your-own-adventure book, or a gerbil, or a human or beyond? Has it given you any useful life advice or anything?
Doesn’t need to be “intelligent”, it needs to be fit for purpose, and it clearly is.
The closest comparison you made was to the cyoa book, but that’s only for the part where it gives me options. It has to have the “intelligence” to decipher what I’m asking it and then give me the options.
The fact it can do that faster and more efficiently than a human is exactly what I’d expect from it. Things don’t have to be groundbreaking to be useful.
To be fair, it is useful in some regards.
I’m not a huge fan of Amazon, but last time I had an issue with a parcel it was sorted out insanely fast by the AI assistant on the website.
Within literally 2 minutes I’d had a refund confirmed. No waiting for people to eventually pick up the phone after 40 minutes. No misunderstanding or annoying questions. The moment I pressed send on my message it instantly started formulating a reply.
The truncated version went:
“Hey I meant to get [x] delivery, but it hasn’t arrived. Can I get a refund?”
“Sure, your money will go back into [y] account in a few days. If the parcel turns up in the meantime, you can send it back by dropping it off at [z]”
Done. Absolutely painless.
How is a chatbot here better, faster, or more accurate than just a “return this” button on a web page? Chat bots like that take 10x the programming effort and actively make the user experience worse.
Presumably there could be nuance to the situation that the chat bot is able to convey?
But that nuance is probably limited to a paragraph or two of text. There’s nothing the chatbot knows about the returns process at a specific company that isn’t contained in that paragraph. The question is just whether that paragraph is shown directly to the user, or if it’s filtered through an LLM first. The only thing I can think of is that chatbot might be able to rephrase things for confused users and help stop users from ignoring the instructions and going straight to human support.
Like a comment field on a web form?
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And it could hallucinate, so you would need to add further validation after the fact
That has nothing to do with AI and is strictly a return policy matter. You can get a return in less than 2 minutes by speaking to a human at Home Depot.
Businesses choose to either prioritize customer experience, or not.
There’s a big claim from Klarna - that I am not aware has been independently verified – that customers prefer their bot.
The cynic might say they were probably undertraining a skeleton crew of underpaid support reps. More optimistically, perhaps so many support inquiries are so simple that responding to them with a technology that can type a million words per minute should obviously be likely to increase customer satisfaction.
Personally, I’m happy with environmentally-acceptable and efficient technologies that respect consumers… assuming they are deployed in a world with robust social safety nets like universal basic income. Heh
You can just go to the order and click like 2 buttons. Chat is for when a situation is abnormal, and I promise you their bot doesn’t know how to address anything like that.
We can! We also know how to use web search, read an FAQ, interpret posted policies…
Some folks can’t find buttons under “My Account” but can find the chat box in the corner.
Also I suspect traditionally, you’ve been able to protect features from [ab]use by making them accessible to agents. Someone who would click a “request refund” button may not be willing to ask for a refund. I wonder how this will change as chatbots are popularized.
I like using it to assist me when I am coding.
Do you feel like elaborating any? I’d love to find more uses. So far I’ve mostly found it useful in areas where I’m very unfamiliar. Like I do very little web front end, so when I need to, the option paralysis is gnarly. I’ve found things like Perplexity helpful to allow me to select an approach and get moving quickly. I can spend hours agonizing over those kinds of decisions otherwise, and it’s really poorly spent time.
I’ve also found it useful when trying to answer questions about best practices or comparing approaches. It sorta does the reading and summarizes the points (with links to source material), pretty perfect use case.
So both of those are essentially “interactive text summarization” use cases - my third is as a syntax helper, again in things I don’t work with often. If I’m having a brain fart and just can’t quite remember the ternary operator syntax in that one language I never use…etc. That one’s a bit less impactful but can still be faster than manually inspecting docs, especially if the docs are bad or hard to use.
With that said I use these things less than once a week on average. Possible that’s just down to my own pre-existing habits more than anything else though.
An example I did today was adjusting the existing email functionality of the application I am working on to use handlebars templates. I was able to reformat the existing html stored as variables into the templates, then adjust their helper functions used to distribute the emails to work with handlebars rather than the previous system all in one fell swoop. I could have done it by hand, but it is repetitive work.
I also use it a lot when troubleshooting issues, such as suggesting how to solve error messages when I am having trouble understanding them. Just pasing the error into the chat has gotten me unstuck too many times to count.
It can also be super helpful when trying to get different versions of the packages installed in a code base to line up correctly, which can be absolutely brutal for me when switching between multiple projects.
Asking specific little questions that may take up the of a coworker or the Sr dev lets me understand the specifics of what I am looking at super quickly without wasting peoples time. I work mainly with existing code, so it is really helpful for breaking down other peoples junk if I am having trouble following.
Cool thanks! I haven’t tried it for troubleshooting, I’ll give that a go when I next need it.
Are you using one integrated into your IDE? Or just standalone in a web browser? That’s probably what I ought to try next (the IDE end of things). I saw an acquaintance using PyCharm’s integrated assistant to auto gen commit messages, that looked cool. Not exactly game changing of course.
I am using the built-in copilot on Vs code + the copilot chat app.
Are you finding that the assistance it provides has gotten worse over time? When I first started using it, it was quite helpful the majority of the time. In truth, it’s still pretty decent with autocomplete, just less consistently good than before. However the chat help has truly gone into decline. The amount of unfounded statements it returns is terrible.
And the latest issue is that I’ve started getting responses where it starts to show me an answer, but then hides the response and gives me an error that the response was filtered by Responsible AI.
Glad I’m not directly paying for it.
So how “intelligent” do you think the amazon returns bot is? As smart as a choose-your-own-adventure book, or a gerbil, or a human or beyond? Has it given you any useful life advice or anything?
Doesn’t need to be “intelligent”, it needs to be fit for purpose, and it clearly is.
The closest comparison you made was to the cyoa book, but that’s only for the part where it gives me options. It has to have the “intelligence” to decipher what I’m asking it and then give me the options.
The fact it can do that faster and more efficiently than a human is exactly what I’d expect from it. Things don’t have to be groundbreaking to be useful.
Smarter than Zork, worse than a human. Faster response times than humans though.
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