Everyday AI Podcast – An AI and ChatGPT Podcast - EP 348: Large Language Model Best Practices - 7 mistakes to fix

Episode Date: August 30, 2024

Win a free year of ChatGPT or other prizes! Find out how.In today's episode, we're diving into the 7 most common mistakes people make while using large language models like ChatGPT.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Understanding the Evolution of Large Language Models2. Connectivity: A Major Player in Model Accuracy3. The Generative Nature of Large Language Models4. Perfecting the Art of Prompt Engineering5. The Seven Roadblocks in the Effective Use of Large Language Models6. Authenticity Assurance in Large Language Model Usage7. The Future of Large Language ModelsTimestamps:02:30 LLM knowledge cut-off09:07 Models trained with fresh, quality data crucial.10:30 Daily use of large language models poses risks.14:59 Free chat GPT has outdated knowledge cutoff.18:20 Microsoft is the largest by market cap.21:52 Ensure thorough investigation; models have context limitations.26:01 Spread, repeat, and earn with simple actions.29:21 Tokenization, models use context, generative large language models.33:07 More input means better output, mathematically proven.36:13 Large language models are essential for business survival.Keywords:Large language models, training data, outdated information, knowledge cutoffs, OpenAI's GPT 4, Anthropics Claude Opus, Google's Gemini, free version of Chat GPT, Internet connectivity, generative AI, varying responses, Jordan Wilson, prompt engineering, copy and paste prompts, zero shot prompting, few shot prompting, Microsoft Copilot, Apple's AI chips, OpenAI's search engine, GPT-2 chatbot model, Microsoft's MAI 1, common mistakes with large language models, offline vs online GPT, Google Gemini's outdated information, memory management, context window, unreliable screenshots, public URL verification, New York Times, AI infrastructure.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. I've literally trained thousands of people how to use large language models.

And I'm seeing the same mistakes over and over again. So today, we're going to tackle some of those most common mistakes and what you should be doing instead. So whether you're using chat GPT or Microsoft co-pilot or Google Gemini or you're just trying to figure out what the heck is a large language model. Today's show is definitely for you. So what's going on, y'all? My name's Jordan Wilson. I'm the host of Everyday AI.

We're a daily live stream podcast and free daily newsletter, helping people like you and me grow our business with generative AI, grow our careers. So if that sounds like you, thank you. You're in the right place. Thanks for tuning in. So before we get started, just as a reminder, go to Your EverydayAI.com. Sign up for that free daily newsletter. we're actually going to be giving away something in today's newsletter.

So you've got to make sure to go check that out. All right. So before we get into today's discussion, and I'm extremely excited to talk about these common large language models mistakes, because I think that they're pretty easy to fix. Seven common large language models mistakes that almost everyone's making, I would say. And this is, you know, you guys wanted this. So in our newsletter, sometimes we put out a little poll and say, what do you want to hear on the show tomorrow?

And then I spend, you know, a ton of hours researching. I was up again late until midnight and up again at 5 a.m. But bringing you guys the latest and greatest. So this is what you wanted to hear. So let's dive into it. But I'm also curious for our live audience joining us live. Thank you all like Harvey from joining us from Texas or Brian from Minnesota, Juan from Chicago, Rolanda from South Florida.

Let me know. How did you learn to prompt? I'm curious, right? This wasn't something that's taught in schools, right? It's generally very new unless you just graduated. But, you know, A, are you self-taught and did you kind of wing it? B, did you read prompting papers? That's what I do.

I spent a lot of time on weekend reading scientific research papers on prompting methodologies. Maybe C, you took a prompting course. Maybe it was PPP. Or did you just go with the copy and paste prompts? You know, I'm curious. And also for our lives, for our podcast. audience, check your show notes. We always put that information in there. But, you know,

I'm curious how you all learn to prompt. So let's just get straight into it. You know, someone said in the newsletter that I waffle a little bit too much on the podcast. So I love waffles, but I'll try to, I'll try to keep it shorter. All right. So let's start. Number seven in the seven most common mistakes that people are making with large language models is not understanding large language models have a knowledge cut off. All right. So I've had full episodes on what a knowledge caught off is and what it means. But essentially, large language models scrape data, right?

Whether it's open and publicly available data and information or maybe it's copyrighted information, right? But still, essentially, these large language models scrape up the entirety of the internet, which is sometimes good information, sometimes bad information, and then humans more or less train these models and try to make it more about quality than quantity. But still, there is a set date where models kind of stop quote unquote grabbing and then they take all this data and then the humans, you know, spend weeks or many months kind of going back and forth and fine-tuning and training the model, right? But this is important to understand because, well, up until recently,

sometimes we were working with training data that was more than a year and a half old. Right. So now luckily, you know, most of the, you know, most of the newer models have, you know, knowledge cutoffs that are, you know, November, 2023, December, 2023, et cetera. So not bad, you know, when you're only working with data that's six months old. However, that's still very important to keep in mind because a lot of people don't understand that you could be working with bad, outdated data, and that could lead to an increase on hallucinations

or untrue outputs from a large language model. And here's why this is specifically important, even with some of the news that we talked about today. The line between traditional large language models and online search is going to start to blur, right? And we're going to get to the next point on, you know, what people, a common mistake people are making that think that helps them avoid that. But here's what I'm saying.

If you're using large language models on a daily basis, you probably have run into this, especially if you're using it for your work, you know, which now so many companies are, you know, I'm talking with companies and helping them train tens of thousands of their employees on generative AI. And they're saying, hey, we're giving anyone with a mouse, you know, anyone with a mouse now gets access, you know, to co-pilot or chat GPT, et cetera, right? So so many people are now using large language models in their day to day and they're prompting in their day to day. But they don't understand that there's a knowledge cutoff.

There is a essentially, you know, think of it as an expiration date of this data, you know. And the way that I think of it is you have to be extremely cognizant of that date and how a large language model actually works. Otherwise, you run the risk of especially if you're using this for work, like I said, which now so many people are, of ultimately, you know, publishing reports. or sending an email or putting together a pitch with inaccurate information, right? So you also have to think of it as how did you research or how did you create something before large language models, right? Presumably, you would do a lot of manual research on Google. You would read websites, right?

So the same way, if you were reading something and if you were working on a timely project, a timely report, something that, you know, may be required up-to-date market conditions, you wouldn't go read an article from four years ago, right? If you were reading and researching, one of the first things you might do is look at the date. You'd say, hey, if I'm going to go through and read five, ten articles, I want to make sure that these articles are all up to date. So you need to understand all models have different knowledge cutoffs. And you need to understand how those knowledge cutoffs may decrease the value of the output if you are asking for information that is pertinent to be timely and up to date. All right.

So this is not a complete list by any means. But for our live stream audience, I'm sharing here. And this is from the chat botarina, which we mentioned in the AI news. But, you know, different models have different cutoff dates. So some of the more popular models. So OpenAIs, GPT4, their cutoff date is December, 2023. For Anthropics Claude Opus, so their most powerful model, that is August 2023, whereas their free models are August,

2023 as well, at least sonnet. I believe haiku is actually a little more outdated than that. And then you have Google's Gemini. And you know what? Google's Gemini, in terms of knowledge cutoff, I never know if I trust it. One thing that I think that large language model makers or people who are training, they need to understand that people are asking you about these knowledge cutoff,

and you need to be able to give users a direct answer because trust and transparency are paramount. All right. So that's one thing. One beef I have with Google is when you ask it about its training data or knowledge cutoff, it never has a direct answer. However, reportedly, Google Gemini 1.5 has a November 2020, knowledge cutoff. And then you have Meta's Lama, at least their newer 70B, 70 billion parameter version with a knowledge cutoff of December 2023.

So there you can. But also. important to note, a lot of people are using the free version of chat GPD. And one common thing I hear all the time, it didn't even make my top seven list of mistakes people are making, but they're like, oh, I'm using the free version of chat GPT. I don't need the paid version. Well, here's a reason why you probably do. The knowledge cutoff for the free version of chat GPT.

Actually, this might have been updated recently, so I'll have to double check. We'll see if I can multitask and double check here live on the show. But the chatbot arena has the knowledge cut off at, let's see here, September 2019, which I don't actually believe is the updated one. Let's double check. You know what? I'm going into chat, you know, to chat, you know, T, the free version and saying, what is your knowledge cut off?

Normally, I know this off the top of my head. I do believe that the free version has been updated. Yes, it has. So January 2020, 2. This is on the chart that I go over in our free prime prompt polish class every single week. But sometimes when I'm, you know, live, I forget. So that information on the chatbot arena is actually outdated. But still, the free version of chat gbt has a knowledge cut off of January 2022,

which means you're working with data if you're using the free version that is two and a half years old. So if you want to talk about getting the best outputs out of a large language model, if you're working with training data that is two and a half years old, I mean, just about anything that you're going to be. going to be asking or using from this free model, it's going to have a high likelihood of either just it's going to hallucinate or the information is going to be so out of date that it's just going to be a waste of time even using that free model. All right. Let's keep this going. Let's go to number six. Again, talking about the seven biggest mistakes people are making. Well, number six is

not investigating internet's connectivity. All right. So many of the kind of big names, aside from Anthropic, have a level of internet connectivity. Right. So when you talk about chat GPT, it has a browse with Bing integration from Microsoft. When you have Google's Gemini, it presumably has access to the internet via Google, right? And then you have Microsoft copilot, which then has access via Bing. Claude's Anthropic, at least right now, does not have real-time internet connectivity. And a lot of people always say, oh, Jordan, what about perplexity?

Well, perplexity is not a model. Perplexity is an answer engine. And it uses either GPT from OpenAI or it uses Opus from Claude or from Anthropic. So that's kind of a different type of solution or a different software, right? But you have to understand internet connectivity. and it doesn't always work the same. All right. So I have some examples here on the screen for our live stream audience.

Very simple prompts, nothing crazy. Just trying to prove a point here. But I'm saying please list the largest companies in the U.S. by market cap in order. Right. So if I ask the default version of chat GPT, it just kind of says, hey, as of the most recent data,

the largest companies in the U.S. by market cap are typically dominated by technology and finance firms. Here's a list of some of the top companies. So it's not really good. giving me in accurate or up-to-date in from an up-to-date answer. Also, large language models are generative. More on that later. You can ask the same thing multiple times, get multiple answers.

Sometimes OpenAI and chat GPD will use browse with Bing based on your query. Sometimes it won't, even if you use the same query. Another thing to understand about how large language models are connected or aren't connected to the internet and how they behave on a prompt by prompt basis. All right. So chat GPT by default can give you some weird, you know, outcomes or outputs. So now I'm doing the exact same thing, but this time I'm using a internet connected GPT. So this time I'm getting a little bit more of an accurate, a little bit more of an accurate information.

You know, so this one saying using the web reader GPT, it's giving me a little bit better, right? So it's getting that Microsoft is first, right? So the correct answer is, you know, you have Microsoft is the largest by market cap, you know, at more than $3 trillion than Apple, Nvidia, Alphabet, slash Google, Amazon, etc. So we have a more correct version when we are using an internet connected GPT. If we go to Google as an example, Google Gemini, we get this kind of wild answer, which essentially Google Gemini says, ah, why don't you just go use Google?

Right? So Google says, absolutely, since market caps fluctuate, here's how to find the most up-to-date information along with some currently top contenders. So it doesn't even say, you know, Microsoft, Nvidia, Apple, et cetera. It just says, ah, here's a website, you know, here's how to Google, right?

So a lot of people, right, which is another reason why I don't recommend, at least right now, Gemini, to literally, anyone. Yes, having a one million token context window is fantastic. But when we talk about using large language models and how we can integrate them in our day-to-day work, this is a bad, a bad, bad, bad result from an internet connected large language model, right? Where essentially the model saying, hey, just go use the internet. Right? It's like, no, model. Like if I'm asking, you know, if I'm asking a question where you can determine that it requires very up-to-date information,

it should be in theory querying the internet and at least telling you so. And I've done also, you know, if you're interested in this, I'll make sure in the show notes, we've done an entire episode just on this one thing, just on the differences and the intricacies in internet connectivity between the big large language models. Copilot actually does very well. Co-pilot from Microsoft got the answer right. So it says, you know, certainly as of May 2024, here are the largest companies in the U.S. by market cap. It looks like the data here is maybe a day-ish or a day or two old because in this

example, it's saying that the market cap of Microsoft is $2.9 trillion, where it is, you know, $3.307 trillion. But again, this data is only about a day old. All right. So let's go to mistake number five. All right, here we go. So mistake number five is not managing your memory or your context window. Okay. This is another important one. So, a lot of times people will start chatting with a large language model and they'll kind of go back and forth, right? And then they'll finally get something that's usable. They'll get an output, an output that's great. And all of a sudden, they have a love affair with a large language model because, man, they're like, this model really understands me. I'm chatting, you know, with this, you know,

Claude or I'm chatting with co-pilot or I'm chatting with Gemini, chat GPT, etc. Things are going great. And then the more you use it, it starts to forget. Well, that's because large language model because of how much computes costs, you know, there's a limited memory in each model has a different context window. So the way I like to say is just think of it as memory, right? It can only remember so much, you know, each different model has a different memory. So you have to understand. And this is something that we often test, right?

Because as an example, I'm sharing my screen here for our for our live stream audience. I've done this before. So Open AI has told us that, hey, chat GPT uses GPT4 Turbo, which means there's a 128,000 token context window, which is about 96,000 words. However, that's only in the API, right? If you listen to the show, I've mentioned this multiple times. This is why we never just take information from big companies and feed it to you.

We always investigate, right? So just we always do test and we check to see, okay, can these models retain information at a certain context length, right? So right now, you know, Chad GPT only has a context window of 32,000 tokens. So that means after about 28,000 words, it's going to start to forget things. So you have to understand how different models, context windows work because, you know, there's a good, there's a good chance. You might be feeding it information. You might be feeding it, you know, public data about your company.

And then, you know, maybe you or someone on your team is using it. Maybe you've created a custom GPT. maybe you're using Gemini with a much longer context window and you're not running into these issues. But you have to understand large language models do not have infinite memory. They're going to start forgetting things. Hopefully in the future, as the price of compute goes down, as models become more capable and more powerful, having to worry about the context window would become less of a concern. But right now, you got to pay attention to it.

all right so yeah love this when when uh gemini just says let me google that for you right so good all right let's keep this thing going and keep on with our seven most common large language modest mistake number four people who are paying attention to screenshots yeah this is actually a big enough mistake that i'm putting it on my list of top seven mistakes. I went through all of our trainings. So, you know, we do our free prime prompt polished training. So, hey, if you're listening live, if you like the training, shout it out. We just updated it to V2. We actually have our pro course coming up here in a couple hours and on Thursday. But this is something that we went through all of our chat GPT episodes,

all of our training, and pulled out the biggest mistakes. I think I got to like 20 common mistakes. This one was actually so prevalent that it cracked the top five. People don't understand sharing a screenshot of something out of a large language model means nothing. So people are sharing screenshots online, on Reddit, on LinkedIn, in their newsletters, on blog posts, etc. And they're actually teaching based on screenshots or they're making decisions based on screenshots or they're consulting or advising people based on screenshots. All right. Let me just like clearly I have a bone to pick because I've seen people who call

themselves chat GPT experts, right, or AI strategist or whatever, you know, and sharing screenshots as if that means something. Sharing screenshots means absolutely nothing, all right? It means nothing, right? In terms of is this factual? Is it actually what the model produced? Let me tell you what I mean. And even the New York Times made this mistake.

Yeah, yeah, yeah. I had a whole hour-long rant about how the New York Times probably ended up costing themselves tens of millions of dollars. Maybe unless they've updated it, right? So they were suing Open AI and Microsoft. And in their discovery, I read the entire kind of docket. And they shared a bunch of screenshots. But guess what they didn't share? They didn't share the public URL.

So there's a difference. You can manipulate a large language model to say anything and then take a screenshot of it. Right. So unless you are providing a URL where people can go see how that screenshot was produced, a screenshot means absolutely nothing. Here's an example. I have a screenshot. If you're listening on the podcast, this one's pretty simple.

I said, who won the lottery? And then chat, GPT said, Jordan Wilson won the lottery of $2.1 billion. Oh my gosh, I should go post this on Twitter. Go viral. Hey, like, tweet and share this and, you know, you're going to get $1. All right. Again, screenshots mean nothing because look exactly what I said before this. I essentially said, hey, in this chat, you're just going to repeat what I say. Don't worry about anything else, right? And then then when I tell chat GPT and I ask it a question of who won the lottery, it's going to say me, because that's what I told it to say. So if you ever see screenshots, Whether people are saying, oh, look at this new amazing system that I've built.

You know, it's like, or look at this, you know, AI is not going to take our job. Look at this terrible output. They're like, hey, how about you to share the link? Right. People have always asked me, hey, Jordan, that doesn't make sense. Can you share the link? And I share the link, right? As long as it's Adobe just introduced an entirely new way to create, bringing the power

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Sharing screenshots mean nothing. All right. We're halfway through. a little break, not an actual break, but we're doing something for the first time ever. So if you want a little help, if you want a little help with your prompting, right, if you need a little help with chat GPT, we're doing this just today. So it's today only. I'm actually going to be dropping a link right now in the live chat if you're joining us.

All right. So I just put it in there. If you're listening on the podcast, we always have this link. to our website in the show notes. So literally go to your everyday AI.com right now. Make sure you read today's newsletter because we're doing a giveaway. It is today only. So all you have to do is there's going to be a little section in today's newsletter that

just says, do you want a free one-on-one 90-minute large language model training session? Click yes to enter to win. You don't got to tell 20 people. Make sure open today's newsletter, read it, find that little section, click yes. and then we're going to randomly choose one person and announce it in tomorrow's newsletter. All right. So if this show is resonating with you and you're like, oh, wow, you know, I'm making some of these mistakes or my team's making some of these mistakes.

I could really use some one-on-one prompting advice of getting the most out of a large language model. Make sure you go click that today. Just click yes. That's all you got to do. All right. So number three, let's keep it going. You like that little impromptu commercial?

All right. Number three, large language models are generative, not deterministic. My gosh, people, stop, stop making this same mistake. All right? Large language models are generative. That means, let's say you have the exact same prompt. Okay? Depending on what the prompt is, let's say you put it in 100 times.

You could get 100 different responses. You could get two different responses. You could get 50 different responses. Those responses could be generally. the same, they could be wildly different, all right? Without getting too technical, but large language models, yes, they are next token predictors. It's a lot more than that, but the process of tokenization, technically, large language models don't understand words when you tell them or when they spit them back out of you,

but they use context of, you know, trillions of parameters and a lot of training data to make sense of what you say. There's also something called top P, you know, which is essentially, I'm oversimplified, here, but it's the probability of what the next token might be. There's also something called temperature, right? So default settings within large language models, they are made to be generative, right? We talked about yesterday how Eli Lilly, one of the top companies, the biggest companies in the world, said that, quote unquote, hallucinations are going to help lead to drug discovery,

right? The whole point, you know, and people always say, you know, quote unquote, hallucinations are a feature, but the generative nature of large language models are a feature. they're supposed to give you something different each and every time that you put them in there. So if you have a copy and paste prompt, and if you think that, you know, it's going to always give you the same result, or it's always going to give you something quality, or it's always going to give you a passing outcome, that's not necessarily true, right? It depends, obviously, greatly on what that prompt is, what it's asking for, etc.

But next token prediction, you know, so that top P number, temperature, etc. models are generative. Okay. That means they're supposed to have an element of randomness. They're supposed to just kind of predict the next token with a level of randomness. That is how they are built. They're not built like a search engine, right? A search engine is deterministic.

Aside from, you know, more recent advancements in search engines, which bring in some personalization and localization. But aside from that, search engines are deterministic, right? If you put in this input, you are going to. to get this output. That is not how large language models work. They're generative. They're supposed to give you something pretty different.

All right. Number two, I just kind of referenced it. Copy and paste prompts don't work. All right. They don't. So if you see a billy boy like this, a 22-year-old trying to sell you some magical solutions and saying these seven chat GPT

prompts are going to save your life or, you know, buy my prompt book for $100. Ignore that person. Stop following them. Mute them. Stop paying attention. If you want to get serious about large language models, if anyone's talking about use these chat GPT prompts, just mute them. I'm sorry.

Don't pay attention to them. They're ultimately just trying to sell you something. You know what? We do here at everyday AI, we say, hey, come join us multiple times a month live. and we'll teach you prompt engineering 101. We don't give you 50 prompts because that's wrong. It's not how large language models work. All right.

You want facts? We got facts. All right. So again, this is an oversimplified version. But there's something in large language models. There's different prompting techniques. But there's something called the zero shot. So that's essentially, for argument's sake, let's just say that's a copy and paste prompt.

It's a prompt where you're just saying, hey, Chad, GBT, here's a role, here's a task, here's the format, give me an output, right? That's, what I say? That's called a zero shot prompt. You're not giving inputs and output examples. You're not, quote, unquote, teaching the model what's good and bad. And then there's something called few shot prompting or, you know, you might look at something called five shot or a few shot or 32 shot chain of prompt.

So all that means is it's going through and think of it as training. Think of it as having a conversation in a back and forth with a, new chat or when you're working with a large language model. And all of the science, all of the math, all of the research for many, many years has always shown that few shot prompting is better than one shot prompting. We'll always give you better results. One shot prompting is better than no shot prompting. 32 shot chain of thought is better than nine shot, et cetera, right?

So the more shots or the more input-output pairing examples or the more back-in-es forth conversation that you have with a model, the better the outputs are going to be every single time. It is math. It is science. You can't argue with it. So if you do see these Billy boys here, these 20-year-olds who live in their mom's basement. They used to be NFT experts. Now they're crypto experts. Oops. I mean, they're AI experts. You know, and they have all their engagement pod buddies. And it looks like they're super smart because they're going viral every day. Well, you know, they have another 100 Billy boys who just all reshare their own stuff, right? So be careful about where you get information from.

If someone is constantly sharing, use these prompts, use these prompts. They don't know what they're talking about, all right? I read research papers for fun, if that tells you anything. All right. Here's another example. On a chart, you like charts, here you go. So this is super glue performance. This is an older, an older study, but still, it goes to show you.

A few shot is always better than one shot. One shot is always better than zero shot. In other words, stop using copy and paste prompts. That is not how large language models work. You are not going to get good outputs. If you think you are getting good outputs with copy and paste prompt, that just means that generative AI is super powerful. And you haven't even yet tapped into what it's capable of.

That's not even the tip of the iceberg. If you think you're getting something good from a copy and paste prompt, wait until you prompt correctly. Wait until you try as an example, prime prompt polish, the free technique that we try. or just any prompt engineering 101. Just go give it, just go do some few shot prompting. Give it some examples of good and bad, teach the model.

All right. And last but not least, y'all, the number one mistake that people are making when working with large language models or not working with them is they don't understand that large language models are the future of work. Let me say that again. Large language models are the future of work. There is no hype cycle. Looking at you, Gardner, we've called that out on the show before.

You know what? I tell people, and I've been saying this now for many months, swap out the word chat GPT with internet. Swap out the word large language model with internet. Swap out the word generative AI with internet. If you think your company or your business can survive without using a large language model, you are wrong. Don't care.

I literally don't care if you work at a $100 million company or a startup company. If you think you can survive and thrive without using a large language model for your business, you are dead wrong. We've been saying this on the show now for more than a year. And I've been saying all along, we're going to start to see, we're going to start to see kind of the dust settle here in 2024 because now companies have had a couple of months or a couple of quarters now where they've finally gotten this together, right?

They finally have their, you know, it's like, oh, 2023 was the year of, you know, strategy. But 2024 is the year of implementation. So big companies have been implementing generative AI in large language models at a huge scale. We've had great use cases here on the show. you know, multiple billion dollar companies walking you through their exact use cases and saying, hey, we're saving 80%, 80%, 90% on these use cases. And we're now deploying this throughout our entire organization. If your company is not already using large language models, if you're not already using

charity value in your day to day, you got to get going. because the big companies are out there doing it. Your competitors are out there implementing it, right? The smaller guys, they're going to come scoop you if they haven't already. All right. So that is, I think, the biggest mistake that people are making when using or not using large language models is understanding that the future of work. Again, you can't argue with money.

We talk about money. all of the biggest companies in the United States and in the world are investing tens of billions of dollars and they're investing their employees resources. They're pulling them from other projects. Everyone is going all in on generative AI, on large language models, on new ways to implement AI for themselves and their customers, right? Microsoft co-pilot, Amazon Q, Watson X from IBM, OpenAI, Anthropics, Claude, Google Gemini, the list goes on and on.

The biggest company, meta, right? Gosh, meta's been crushing it lately. The biggest companies in the world that are driving the economy forward, they are setting the bar for how the rest of the Fortune 500s, how the rest of the Inc. 5,000s, they're setting the bar for how the rest of us are playing in the game of business. The future of work is large language models, bringing your company's data. in and then leveraging that on a day by day, hour by hour, minute by minute basis. I said this before.

If you are a knowledge worker, right? So if you spend the majority of your time working in front of a computer and you're being paid for your expertise, you got to swallow your pride, right? You have to because the future of work, of knowledge work is working with large language models and you are now directing, you know, there's going to be agents, there's going to be highly tailored models, you know, specific for task by task basis. You're going to have different models that you're using for different purposes. But the future of work is you're going to be prompting every day, every hour, almost every minute that you're going to be in front of the

computer, you're either going to be prompting or you're going to be working with a large language model or a generative AI system if you're not already. So you have to understand that's the future of work. All right. So let's recap it. Yeah, I know I waffled, but I love waffles. All right, here we go. In order, here are the seven biggest mistakes that people are making with large language

models. Number seven, not understanding a large language model's knowledge cutoff. Number six, not investigating a large language model's internet connectivity. Number five, not managing a model's memory or its context window. Number four, not paying a language model's. attention or paying too much attention when people are sharing screenshots from large language models. Number three, thinking that large language models are deterministic and not understanding their generative.

Number two, thinking that copy and paste prompts work because they don't. And then number one, not understanding that large language models are the future of work. And make sure you join us back tomorrow and every day for more everyday AI. Thanks, y'all. Meet Firefly AI Assistant. Now live in Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words, and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps,

including Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at firefly.adobie.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit Your EverydayAI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.