Ep 310: The One ChatGPT Mistake That We‘re All Making

ChatGPT: A Shortcut to Efficiency or a Challenge to Skill Development?

The age of Artificial Intelligence (AI) has unleashed unprecedented opportunities to automate tasks and skyrocket efficiency. Particularly, the use of large language models like ChatGPT has emerged as a potential game-changer in today's business dynamics. Yet, amidst the allure of the tech revolution, the value of expertise, skill, and human finesse seems to be undergoing a critical test.

Analyzing the Current Workplace Scenario

Tailored with the ability to outperform humans in narrow tasks, AI has gradually begun to replace certain segments of the workplace, undercutting established professional expertise. A significant shift from traditional working models to AI-centric ones has been observed, with promotions and rewards becoming highly reliant on the integration of AI into the existing skill set. However, should AI be seen as a tool for replacement or a resource to better one's abilities?

Turning AI from Substitute to Collaborator

It is important to refrain from merely substituting AI for one's skill set. The aim should be to utilize AI to enhance and bolster skills rather than diminish them. Ideally, AI, such as ChatGPT, should be looked upon as a consultant, incorporated through specialized prompt engineering programs to supplement the workforce. This approach, if correctly implemented, could significantly improve overall output, with the synergy of human skills and AI resulting in a more qualitative and quick delivery.

Mistakes to Avoid with AI Integration

Contrarily, the misuse of large language models in the present time is quite alarming. Instead of using AI to spark critical thinking and research, it is often deployed as a tool for 'copy-and-pasting' tasks, leading to a decline in long-term core skill development. It indeed presents a severe conundrum - short-term productivity against long-term skill growth. The excessive dependency on generative AI not only leads to complacency but also threatens the richness and depth of one's skills and knowledge.

Using AI Wisely: The Choice Is Ours

The concept of AI-powered efficiency shouldn't result in complacency or laziness. A tactic such as the RefineQ technique – which emphasizes the need for providing thorough information and asking the right questions when employing large language models – can be beneficial. Generative AI should be used wisely to aid work and not as a shortcut for more expedient results.

Preserving and honing our abilities should remain utmost. While AI can facilitate productivity, it is vital not to surrender the craft entirely to automation. Becoming better at one's craft involves not only using AI to augment work but also maintaining the practice of skills to prevent erosion over time.

In conclusion, it's time we reconsider how we're positioning AI in today's business model. The integration of AI should not eclipse one's skill sets but rather illuminate and enhance them. After all, an investment in knowledge and skills pays the best interest. Prioritize long-term growth over immediate efficiency, and remember, artificial intelligence is no match for natural stupidity. Use AI wisely.

Topics Covered in This Episode

1. Current Role of AI and Skills
2. Future Impact of AI on Employment
3. Misuse of AI and the Potential Effects
4. Harnessing AI for Skill Improvement

Podcast Transcript

Jordan Wilson [00:00:15]:
There's 1 ChatGPT mistake that we're all making. Well, not just ChatGPT, but large language models in general. And it actually has very little to do with prompting, and it has everything to do with mindset and with our skills and with our abilities. And if I'm being honest, I think large language models are actually making us lazier, and they're making us kind of dumb. But it shouldn't be like this. There's more to using large language models than meets the eye. And I've literally helped thousands of people learn ChatGPT and other large language models. And I'm gonna be sharing with you today the 1 ChatGPT ChatGPT mistake that I think we're all making and how we can change it.

Jordan Wilson [00:01:04]:
Alright. I hope that got your attention. I'm excited for today's show. So if you're new here, what's going on? My name is Jordan. I'm the host of Everyday AI, and this is a daily livestream podcast, free daily newsletter, helping us all learn and leverage generative AI. This is, a livestream. This is unscripted, unedited, the realest thing in artificial intelligence. So if you haven't already, why the heck not? Go to your everyday ai.com.

Jordan Wilson [00:01:27]:
Sign up for the free daily newsletter. We'll be recapping today's show and a whole lot more, including bringing you the AI news. So it is our hot take Tuesday. On Monday, we bring you what's happening in AI news and why it matters, and on Tuesday, we bring you hot takes. So for our livestream audience, at least, let me know. I could get a little cranky today. You know, should I keep it hot? Should we go, kind of soft? Let me know. I already said wireless models are making it dumb, so apparently, I'm a little spicy.

Jordan Wilson [00:01:59]:
Alright. But let's go ahead. And before we get started, let's go over and start as we do every day with going over the AI news. And, hey, livestream audience. Take a look

Jordan Wilson [00:02:11]:
at your take a look

Jordan Wilson [00:02:11]:
at your screen there. Alright. Let's, in AI news, a lot going on today. But, 2 powerful new AI video tools have just been launched. So first, there's Dreamflare AI. It's a startup that aims to assist content creators in producing and monetizing short form AI generated content. Dreamflare offers 2 types of animated content, flips, which are comic book styles, stories with AI generated short clips and images, and spins, which are interactive choose your own adventure short films. The other new AI tool that just launched is Odysee, a San Francisco based startup that just came out of stealth mode with $9, 000, 000 in seed funding from GV and other in GV and other investors, which is aiming to revolutionize the film industry with advanced AI technology.

Jordan Wilson [00:03:01]:
They're looking to be a Hollywood grade AI video tool in line with Sora, Runway, Cling, and others. So we'll obviously have, previews of that in the newsletter. Here's a fun 1 y'all. The New York Times may be suing OpenAI, but it is reportedly using the AI tool to help write its headlines. Yeah. Alright. So according to reports, The New York Times has been using OpenAI's technology for headline generation and enforcing its style guide as uncovered in a leaked code repository on get on GitHub and first reported by The Intercept. So the leak code exposed The Times experimentation with AI tools, including a headline generator and style guide checker.

Jordan Wilson [00:03:45]:
So The Times' alleged use of OpenAI highlights the evolving role of AI in newsroom tasks traditionally carried out by human editors. So this 1, obviously just goes to that legal battle, right, between the Times and OpenAI and underscores the significance of the copyright infringement allegations in the context of AI integration into journalism. So, yeah, if this is true, that might really hurt The New York Times' case against OpenAI if they're still using its platform. I don't know. When they did request that the technology be destroyed in this lawsuit. Alright. 1 more piece of AI news and still, hey, OpenAI. A lot of lot of big news from them today.

Jordan Wilson [00:04:24]:
So OpenAI and Thryv have just launched a new AI powered health company. So Thryv AI Health, a new start up backed by OpenAI's venture fund and Thryv Global, aim to democratize access to expert level health coaching using AI to address health inequities and chronic diseases. So DeCarlos Love, a former Google product leader, joins as CEO to lead the development of a personalized AI health coach targeting behavior change across sleep, food, fitness, stress management, and connection. So this is going to be leveraging resources from OpenAI and Thrive Global. The company plans to empower individuals through AI driven health coaching focusing on prevention and disease treatment optimization. Alright. So a lot of a lot of news there, going on and,

Jordan Wilson [00:05:14]:
yeah. But that's not why

Jordan Wilson [00:05:16]:
you tuned in. Shout out to our livestream audience. Get your vote in now on this, secret poll. But let's just get straight to it. Alright. Let's talk about the 1 ChatGPT mistake that we're all making. And, again, to reiterate, this is not just ChatGPT, but I'd say it's the most widely used large language model. But here's our here's our takeaway.

Jordan Wilson [00:05:38]:
Here's the end. We're using large language models to become more efficient in our current skill set. Right? But that's the absolutely wrong way to use a large language model. Doesn't sound

Jordan Wilson [00:05:57]:
like a smart take from me. Right? Like, wait.

Jordan Wilson [00:06:00]:
Isn't the whole point of using generative AI? Isn't the whole point of using large language models is to increase productivity and to increase efficiency? Kind of.

Jordan Wilson [00:06:12]:
But we're becoming overreliant, and I

Jordan Wilson [00:06:16]:
think that as a society, maybe, and, you know, obviously, I'm speaking to our audience here. Right? I'm I'm speaking to, you know, our our livestream audience, people such as as Michael and Fred and Tara and Jay and Colby and Rolando and Ben. Right? All of us out there who are using AI on a daily basis. And I'm putting myself in there as well. And this is something I always have to remind myself because it's a big mistake. I think we're choosing or we're opting for short term productivity, and we're sacrificing long term core skill development. Yeah. I have to pause and and think about this because I think it's actually problematic.

Jordan Wilson [00:06:59]:
Right? And I think, as humans, we can be lazy. Right? Kobe said to bring the heat, so did Tara, so maybe I'll bring some heat here. As humans, we are inherently lazy. Right? You always look for the fastest way to do something. You look for the easiest way. You look for shortcuts. Right? That's why I think generative AI has been such a huge boom. Right? Because it allows, people to do their jobs so much faster.

Jordan Wilson [00:07:34]:
Right? And especially for those that, you know, their generative AI use is kinda under the table or companies are like, yeah, you can use it, but they're really not pushing and they're not educating their employees. Those that have figured it out are all of a sudden like, yo. Like, I can get my job done in a 4th of the time. This is amazing. Right? These reports take minutes instead of, hours. Right? This data analysis takes 30 seconds instead of half a day.

Jordan Wilson [00:08:04]:
The people that are using generative AI are choosing short term productivity. And there's nothing wrong

Jordan Wilson [00:08:12]:
with that. Right? Because prior to generative AI, that's what it was all about. Right? It was all about, hey, you know, how can you use marketing automation? You know, how can you use all these new, you know, software as a service? And you might be thinking, okay. Well, what's the difference, and why is that a problem, Jordan? If we're using large language models to just be more efficient and more productive, why is that a problem? Because historically, that's how it's always been. Right? You use the latest technology. You use the Internet. You use cloud. You use mobile.

Jordan Wilson [00:08:48]:
Right? You use what tools and technologies are available to become more efficient and more productive. Yes. But generative AI is a little different. Right? Because as an example, the Internet didn't really do your job for you. Right? I mean, some people yeah. Sure. But for the most part, right, when we look at knowledge workers here in the US, that's the majority of our audience. So if you sit in front of a computer every single day and you are paid for your expertise, you are paid to whatever your expertise may be.

Jordan Wilson [00:09:21]:
It could be, data entry. It could be project management. It could be marketing automation. It could be so

Jordan Wilson [00:09:28]:
many of these things. But, historically, the Internet didn't change that too much. Maybe it made it easier. But the difference is with generative AI is it's actually, for the first time ever, starting to do our job for us. Right? And therein lies the problem for our long term skill development. And this is where I think we're overlooking even the meaning of a large language model. They are literally trained in their system prompts to be helpful assistants, yet we're not using them to be helpful. Assistance.

Jordan Wilson [00:10:09]:
We are just giving them certain blocks of our work, trying to find the shortest kind of like the combination of the easiest prompt that has the best results. And then we are trying to give the models as much of our work as possible.

Jordan Wilson [00:10:28]:
And, again, it's not bad, but it's the wrong way. Alright?

Jordan Wilson [00:10:36]:
And, hey, I'm gonna I'm gonna say this right now. Take a little, 10 second, commercial break here. You're probably gonna wanna repost this episode. Alright? So if you're joining us live on LinkedIn, go ahead, click that little repost button now, because this problem, I'm gonna help a couple people. Right? So, if you're listening on the podcast, make sure to check out your show notes. We're gonna have a link, to this LinkedIn post. We go live on LinkedIn, Twitter, YouTube, but you can't share these on YouTube. So make sure to go check

Jordan Wilson [00:11:08]:
out the LinkedIn post and repost this. Alright? Because we're gonna help,

Jordan Wilson [00:11:12]:
I think we're gonna do 3, kind of short 1 on 1 sessions to help you get over this problem, because we've helped thousands of people. But more on that later. But let's dig into a little bit more here on this problem. Because like I said, this is how almost everyone is using wire language models right now. Right? Here's my work. Go do it. So let's talk. Let's go through an example.

Jordan Wilson [00:11:45]:
Right? And probably the easiest example I know this might be a lazy example, but let's say you are a writer, a marketer, someone in communications, PR, etcetera, and you are using, let's just say, ChatGPT to do your writing for you. Right? This is 1 of the earlier use cases of generative AI technology. Right? This is even what our team started using, the GPT technology for, right, when it first became publicly available in late 2020. Right? It was essentially a copywriting tool early on through the GPT 3 technology in tools like, you know, CopyAI, Jasper. You know, we mentioned some of those because our our team was using those almost daily back in 2020. Even my background. Right? Talk about this a little bit, but my background's in journalism. I've been getting paid to write professionally for more than 20 years, some of the biggest brands in the world.

Jordan Wilson [00:12:49]:
Commercials, you know, big brands, like everything I've been getting paid to write for 20 years. So, you know, these early tools, it was easy to be like, oh, wow. We can hand off a lot of our copywriting. Even some of the earlier tools. I mean, they they looking back at them now, they were absolutely terrible. But they can handle a skill like copywriting. It's something large language models are great at. So let's use that as an example.

Jordan Wilson [00:13:12]:
Alright. So let's just say for whatever reason you are a writer. And let's say your current skill set or maybe, you know, you're a marketer and writing is 1 of your big jobs. But let's say that you currently have a skill set 6 out of 10 in writing. Alright? Hopefully hopefully, we're tracking,

Jordan Wilson [00:13:32]:
we're tracking here on this 1. So you have a skill set 6 out of 10. But then guess what? These large language models are pretty good. Right? And as a writer, especially writers who have been

Jordan Wilson [00:13:45]:
doing it a long time, you can spend

Jordan Wilson [00:13:48]:
hours, literally hours on 1 paragraph. You could

Jordan Wilson [00:13:53]:
spend an hour toiling over 1 word, right, to make it better. Right? I gotta cut the fat. I have to, you know, this this these 2 paragraphs, we need to transition, between these 2 paragraphs. It's kind of abrupt. Right? Like, there's an art and a science of writing, but let's just say you're a 6 out of 10 and you're handing it over to Chad GbT, and you're like, woah. This Chad GbT thing's pretty good, or this Claude 3.5 sonnet is is pretty good. I'm handing over my copywriting, all of it to them. Alright? But then we have to think about why.

Jordan Wilson [00:14:27]:
Right? Because then, essentially, you're just using a large language model to write more copy faster with a simple copy and paste prompt. Right? That's ultimately what what we do. Like I said, we're lazy humans. We look for shortcuts. We say, hey. What's the best prompt or the best the best methodology that's quick and easy that

Jordan Wilson [00:14:53]:
I can

Jordan Wilson [00:14:53]:
get the best results? That I can just put this, copy and paste this, and, oh, boom. My 8 hour job is now 1 hour. And maybe I'll tell people, maybe I'll take on more work, maybe I won't. Right? But that's what so many of us are

Jordan Wilson [00:15:07]:
doing. Okay?

Jordan Wilson [00:15:11]:
So, I mean, here's here's the the goods the good part is you'll be able to wait, you'll be able to write way more copy at that current skill set. Like I said, you're a 6 out of 10.

Jordan Wilson [00:15:25]:
What about the downside though? Right?

Jordan Wilson [00:15:29]:
We don't think about that if we become over reliant on large language models, which is part of the problem. Right? It's part this is this is the big issue here, is we are becoming too reliant. So if you are that skilled copywriter,

Jordan Wilson [00:15:48]:
6 out of 10, guess what? Your skill set is going to go down over time.

Jordan Wilson [00:15:58]:
Right? If you think that the more and more you use ChatGPT or use a large language model to do your work for you. Let's say you're in data analysis. Let's say you are, in web development and and and you're you're you're coding. Let's say you are a strategic creative. Right? Whatever it is. There's always a con. If you are using ChatGPT just to do your work, just to replace your skills, A simple copy and paste prompt. Your own skill set is going to start to deteriorate.

Jordan Wilson [00:16:38]:
So if you are a 6 out of 10, and you've, you know, learned to pump out some, you know, c plus, b minus content in 1 tenth of the time, you're gonna keep doing it. Right? What happens to your skill set? After a week, are you still a 6 out of 10? After a month, are you still a 6 out

Jordan Wilson [00:16:59]:
of 10? After a quarter, are you still a 6 out of 10? Or do you start to lose your skills? Right? So many of the high demand skills that

Jordan Wilson [00:17:08]:
we get paid and we get rewarded, for being experts,

Jordan Wilson [00:17:15]:
We do so by keeping those skills sharp. Right? By practicing them daily, hourly, by the minute.

Jordan Wilson [00:17:24]:
That's what got us in the position where we are today. Whatever your role is, whether you've been there for for 2 months or 20 years, there's a good chance that you're there because of your expertise, because you've gone for maybe years, maybe decades. You've gone through these manual steps of building your expertise, becoming a subject matter expert in something. So now we're at this crossroads, right,

Jordan Wilson [00:17:51]:
in this new economy, in this new AI first, AI native economy, where things are different. We're not going to in the future, I've said this, in

Jordan Wilson [00:18:00]:
the future, we're not going to be rewarded, promoted for what we know. We're not gonna be promoted for our current skill set or rewarded. We're gonna be rewarded and promoted for how we use that with AI. Can we get the most out of AI leveraging our former skill set? Right? But you're going to lose it. If you just hand your skill set over, you're gonna lose it. I've talked about this before. Right? That's 1 of

Jordan Wilson [00:18:30]:
the reasons why I still write the daily newsletter by hand.

Jordan Wilson [00:18:36]:
Yeah. It's it's almost like vintage. Right? You know, I was I I was joking around with, a a good friend of mine, about a month or 2 ago. You know? And we said, hey. In the future, it might be a you know, let's just say you buy, I don't know, an app on your phone, I think was his example. And he said, you know, there might be a market in the future where, oh, this was made by humans. This was made without code. Right? And it's almost like, you know, like vintage or like a throwback.

Jordan Wilson [00:19:07]:
Right? Like buying a record. Right? But still right now, I write the daily newsletter for our right? When I'm done here, I'm gonna go and write the top takeaways that we got to in this episode. I might answer a couple of the questions that come in from our audience. I might answer that in the newsletter, but I write it. Right? Why? Because that's my core skill set. 1 of my core skill sets is being able to tell stories, being able to write, being able to craft compelling copy. Can ChatGPT do it better than me? Absolutely. But if I just hand off and and do I use it for other aspects? Absolutely.

Jordan Wilson [00:19:43]:
Right? But if I hand off 1 of my core skill sets, if I'm a 6 out of 10, I'm gonna start to lose that skill set. So here's what we

Jordan Wilson [00:19:53]:
need to do, y'all. Here's what we need to do. We need to turn ChatGPT into

Jordan Wilson [00:20:01]:
a consultant. Alright. And I'm gonna walk you through what that means. And if you've recently taken our PPP course recently, right, because we updated it a couple months ago, that's our free prime prompt polish prompt engineering course. This next part might sound familiar. So if you're on the podcast, make sure to just literally, or anyone, if you want access, probably the easiest way, I mean, you can type in PPP, in the chat here. And, you know, if you have taken the course recently, let me know what you think in the comments if you're watching live. The easiest way if you wanna get access, we don't put it on our website.

Jordan Wilson [00:20:41]:
It's kinda secret. It's hidden. Just subscribe to our newsletter, your everydayai.com. Reply to any of the emails. Just put PPP and I'll send you the link. Alright. But if if you have taken our course, this next part will probably be familiar. So, like I said, we've taught I have to actually count.

Jordan Wilson [00:20:59]:
We're probably close to 6000. 6000 people live. We do this live multiple times a week. I'm gonna be doing this, in about 3 hours right now. We teach people the basics of prompt engineering. But the biggest thing is turning ChatGPT into a consultant. Alright? Because going back to our previous example, if I'm a if I'm a copywriter that's a 6 out of 10, and if I just am handing my work off, my skills are gonna go down. Right? I'm sacrificing short term productivity for long term skill development.

Jordan Wilson [00:21:34]:
I'm eventually gonna be a 5 out of 10 and

Jordan Wilson [00:21:35]:
a 4 out of 10 the more I use AI. Right? But and and and the

Jordan Wilson [00:21:40]:
other thing too, if I'm a 6 out of 10 and I'm using a simple prompt, right, but think in your own head, whatever you use a large language model for. Right? Sales prospecting, research, competitive analysis, like whatever you're using it for. If you're just finding the simplest copy and paste prompt, your skill set's actually going down. Not only that, but your output or what ChatGPT or a large language model is capable of is going to be capped as well. It's gonna be capped at your current skill set. If you're just doing a simple copy and paste

Jordan Wilson [00:22:11]:
prompt, if you turn ChatGPT or a large language model into a consultant, your skill sets are going to increase and

Jordan Wilson [00:22:25]:
the output of the model is going to improve. Y'all, it's simple math. It's simple science, but we overlook it because we are lazy humans. And we say, what is the fastest, shortest, easiest way to give my work to this large language model and have it be done? But remember, it's a helpful assistant. So in our prime prompt polish course, we talk about priming and how, essentially, your output out of a large language model is dependent on the work that you put in. Right? In these models, there's a lot of garbage in there. Right? When we talk about these ultra jumbo models with trillions of parameters that have just scraped the entirety of the Internet. There's a lot of bad information out there.

Jordan Wilson [00:23:12]:
So depending on how you're using it, you might just get not that great outputs, especially if you're just trying to copy and paste prompt your thing, if you're trying to 0 shot your way to something usable, if you're trying to few shot your way to business growth, that's not how it works. You have to turn ChatGPT into a consultant. We teach refine queue. It's a priming technique. So before you ever ask for an output, you gotta put in you gotta put in a lot of work, a lot of back and forth, essentially conversation. We have this a podcast episode a couple of months ago with, Abram from OpenAI, and we talked about this exact concept. Right? And how people are getting bad results out of large language models because they're just putting a simple prompt in there, and they're not taking the time to essentially train the model or train the response or coach or teach a skill set. That is what we need to do.

Jordan Wilson [00:24:07]:
And when you use the refine cue method, right, so that's assigning a role, giving examples, fetching information, asking for insights, narrating the pain points, explaining the expectations, and ending with questions. Right? So that is refined cue. The biggest part is asking questions when you are using a large language model. So if you don't get anything out of this long winding podcast livestream today, get this. Do not ask for an output when you start a conversation in ChatGPT. Turn it into a consultant, give it all of the information it needs, and then tell it to poke holes. Right? That is what RefineQ is. You do not want an output.

Jordan Wilson [00:24:56]:
You want to turn ChatGPT into a consultant. Because, again, not only is that going to make your output exponentially better, whatever you're doing, and then you will be able to reuse it and scale it, but you as a human will get smarter. It's problematic. AI is so good. It's problematic. We're handing everything over to AI, but it can make us dumb. This is, I think, the single biggest problem of large language models. It's we're not using them

Jordan Wilson [00:25:34]:
the right way. Right?

Jordan Wilson [00:25:37]:
So we need to turn it into a consultant. And I'm gonna not poke fun, but, hopefully, I'm gonna illustrate a point here. Because, you know, some of the biggest consulting companies in the world, when you go back to 2021 or 2022 when ChatGPT came out, a lot of the biggest consulting companies in the world wrote off generative AI. They wrote off large language models.

Jordan Wilson [00:26:08]:
They said this isn't a threat. They literally go back and read it. We we have receipts all day.

Jordan Wilson [00:26:15]:
Right? They literally advise their clients, do not use AI. This is not a technology you should be using. Right? I poked fun of them, you know, a year and a half ago on the show when even still, right, in early 2023,

Jordan Wilson [00:26:33]:
companies were like, some of the largest consulting companies in

Jordan Wilson [00:26:37]:
the world were like, no. This large language model thing, don't worry about it. Don't think about it. Why? Because they

Jordan Wilson [00:26:43]:
understood, if used correctly, it could do a bulk of the work that they were doing. Yeah.

Jordan Wilson [00:26:53]:
There's kind of some some early articles floating around, you know, when people finally understood the basics of prompt engineering. Right? Oh, I have to work with the model a little bit, and I can turn it into a consultant, and it's gonna make me better. You know, people started saying, oh, large language models are like having a consultant in your pocket. Right? Yeah. And eventually eventually, these big consulting companies realize, oh, we're losing a bunch of clients. You know? And and the public consulting companies were like, oh, our forecasts aren't going very well. You know, our shareholders apparently aren't happy when we say, ah, this AI stuff, don't pay attention to it. Large language models, they're dangerous.

Jordan Wilson [00:27:36]:
Right? They're not very good. We're the best. Right?

Jordan Wilson [00:27:38]:
Of course, consulting companies were writing it off. Right? They felt threatened. If you don't understand a technology or if it's really good, you feel threatened, and you tell people, ignore this. This AI stuff, not very good. Hey, small medium business. Hey, enterprise company that's been 1 of our overpaying clients for decades, don't use AI. You need us. ChatGPT, this thing is large language models, they're just for blog posts.

Jordan Wilson [00:28:06]:
No. They're not. Absolutely not. Large language models, when used correctly, outperform the smartest humans, period. In narrow tasks, there is no when used correctly that's the thing. 99% of people aren't using large language models correctly. The ones who are aren't growing their companies. They're growing their careers.

Jordan Wilson [00:28:31]:
They're growing their departments. But, eventually, these consulting companies, they couldn't run from it anymore. Right? And they realized, oh, yeah. These large language models, if you do a little more than just asking for a simple prompt, they're actually really good. Yeah. Right? Yeah. After writing them off for so long, they're like, oh, yeah. Turns out the first companies that started investing in AI and they started mentioning it, and, oh, all of a sudden, their business grew exponentially, bringing in tens of 1, 000, 000, 000 of dollars in more revenue because they started to, hey.

Jordan Wilson [00:29:06]:
We should use AI. And it's actually pretty good as a as a strategic partner. Right? As a helpful assistant, not as just copying and pasting your current skill set. Because now look what's happening. Oh, PwC is investing $1, 000, 000, 000 in AI. Deloitte investing $2, 000, 000, 000 in AI. Accenture investing $3, 000, 000, 000 in AI. Guess what? They figured it out eventually.

Jordan Wilson [00:29:39]:
The future of work is with generative AI. And when you use large language models correctly, when you use them what they were built for, to be a helpful assistant, they're not only going to grow your own skill set, but your output in the model is going to be unrecognizably better.

Jordan Wilson [00:30:01]:
So humans, can we all just do something? Can we stop being lazy?

Jordan Wilson [00:30:09]:
I'm calling myself out as well. I mean, is it nice to jump into a a Copilot or a Gemini or a Claude or a ChatGPT and put in 1 little prompt and have it do, you know, 3 hours of work in 3 minutes, absolutely. That's great. Right? But at what cost? Right? Especially, if you are using it consistently for that 1 skill set, that is your expertise. And I wanna clear this up. I'm not telling you. Right? I gave the example of myself. Yeah.

Jordan Wilson [00:30:45]:
I write the newsletter. Because that that part for me is important. Writing in clear communication is important because prompting large language models requires clear written communication. So that part's important. Alright? So I'm not trying to dissuade you from using large language models to do your work. It's the exact opposite. You should be doing it, but do not do it in a copy and paste fashion. Do not do it in a lazy fashion.

Jordan Wilson [00:31:16]:
Do not do it put in a 6 out of 10 effort, the the the easiest copy and paste, and just ask for an output, because you are missing out on the most powerful aspect of large language models, which is it will help you be better. Right? When you tell ChetGPT or Claude or Gemini to be a consultant, when you go through, as an example, our RefineQ process, it makes you think.

Jordan Wilson [00:31:49]:
All of a sudden, if you do this correctly, you have a seasoned consultant at PwC on

Jordan Wilson [00:31:56]:
the other side. You have a seasoned strategist at Accenture on the other side. You have AAA wise Deloitte leader on the other side, asking you questions, poking and prodding, forcing you to do more research. That's the key. It's saving you time, making you smarter, and improving your output all at the same time if you do it correctly. So don't just use chatGPT to do your work. Use it in other large language models to make your work better,

Jordan Wilson [00:32:38]:
to make you smarter, To make you the AI leader in your department, in your organization, in your company. Use it

Jordan Wilson [00:32:47]:
the right way. Don't make the same mistake that everyone else is using or that everyone else is making. Right? Use large language models. The way that they were built to be used, to make you smarter and to make your work better and to save you time. Alright. I hope that was helpful. You know, little bit of a hot take Tuesday. We got a little bit spicy.

Jordan Wilson [00:33:13]:
Right? But for our livestream audience, appreciate you tuning in as well. Podcast audience. Hey. I told you, go check out the show notes. Come back to this LinkedIn. Click repost. So we're gonna be doing, 3 consults that we generally charge 100 of dollars for, and we're gonna work with you 1 on 1 to help you avoid making these mistakes. Whatever your large language model live of, whatever your large language model of choice is, we can help you get better at it.

Jordan Wilson [00:33:44]:
We can help you work through problems. Right? So whether it's your your prompting technique, how to bring in more accurate up to date information, how to, you know, maybe as an example, you're using Claude, and you're like, oh, wow. I've heard about this new feature, but I don't know how to use it. Whatever your large language model question is, we can help you. Alright? And, yes, we, companies or individuals, generally pay us 100 or 1, 000 or tens of 1, 000 of dollars, right, for some of our time. So we spend a lot of time here at Everyday AI to bring you high quality information. So we just ask if this was helpful.

Jordan Wilson [00:34:22]:
Click that repost button. Takes you 4

Jordan Wilson [00:34:25]:
to 5 seconds. Alright? And we spend 4 to 5 hours sometimes on shows like today's. So anyone that repost this on LinkedIn, you will be entered, into a drawing where we're gonna be giving away, 3 quick 1 on 1 consults to get more out of large language models. So I hope that's you. Takes takes a couple seconds. So if you're on the podcast, please, super simple. The link is gonna be in the show notes so you can go, to this exact episode. Click that repost button, and you will be entered to win.

Jordan Wilson [00:34:56]:
So I hope this was helpful. Hey. In livestream audience, let me go back here. Let me go back here. Sorry. We got a lot of flashing going on. But, tomorrow, you're deciding what's on the plate. So, let me know.

Jordan Wilson [00:35:12]:
Thank Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.

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