Why and When You Should Use OpenAI o1

So today I have a bit of a special upload for you. As you know, recently OpenAI released the O1 preview model along with O1 mini. And let me tell you that after a few weeks of using this model in my day to day workflows and leading endless discussions around what to use it for, I came to some interesting and unique conclusions. What you actually want to be using this model for. And I collected all of that knowledge into a lecture that I presented to the AI Advantage community. This is one of 15 to 20 events that we actually run inside of the membership every single day. As you can see here in the schedule, this is the LLM Innovations format. But we also have the same thing for the Creative District, which is all about image and video generation, and the no Codes district, which is all about external applications that use AI for various workflows. Then we also have foundational events for beginners, panel discussions and office hours, which are like group consultings. Now, as you might know, that is our paid membership. And usually all the lectures in there are behind a paid wall. But this one in particular I really wanted to share with all of the subscribers on YouTube because I feel like it works by itself. Itself, a lot of the community content falls into a longer learning path that takes multiple hours to complete and to actually build a skill. But this event really talks about the basics of 01, where to use it, how to use it, and concrete examples of when it's superior to any other LLM out there. And that's why you're seeing it here on YouTube. So without further ado, let's get into this month's edition of LLM Innovations, the lecture series that I hold every single month inside of the AI Advantage community. I want to start by showing you what why I was super excited to do all the research for this lecture, or what got me so excited because I found that one of the main tasks that I keep returning to with ChatGPT is actually better performed by O1. So without further ado, let me share my screen and let me just show you the result of what O one can kind of do for you here. And I'm going to do it always in this format. I always have two conversations that I kind of prepared in advance. One of them is in 4.0. Let's keep that on the left side. And one of them is in 01. Let's keep that on the right side. The task that I'm referring to, there's some other examples. I have those we're going to talk about real world Use cases. That's really the main focus of these presentations. But the task that I'm referring to is rewriting text. It's state of the art at rewriting. Let me just back up for a second and tell you that already with something simple like this, right? Just make it better. Sure, you could be more precise with that, but I kept it quite generic here. This 01 Concretely, 01 mini result, I always prefer to O1 Preview and GPT 4.0 and Sonnet and anything else on the market. It's actually crazy how consistently good it is. I'll point out the nuances that make it better here in a second. But let me just tell you about my general workflow with writing these days. It always starts with original thought. I do not begin inside of the AI. We talked about this before, but we're just at a point in time and if you let AI do all the writing for you, it's not going to be good enough. Certainly not by my quality standards. But I think society in general just agrees that, you know, AI written text, standalone is just, you know, if you're using it in a professional environment, probably not good enough. So I always start with my own writing. Even if it's super simplistic, even if it's just a brain dump where I. And look, don't worry, this is not going to be some like writing with AI Masterclass. I just want to outline the process I usually do. Even if it's just this brain dump or a bullet point list, or I just throw down all the ideas that I have. I take those. Then I go to Sonnet 3.5. I use a very simplistic prompt there to rewrite it. Maybe I specify a little bit of myself. Sometimes I give my custom instructions, but usually I just tell make this flow well, like that's a really good universal prompt that just works. And then Sonnet does that. Then I take that. Maybe I make a slight edit, mostly not. I bring it into ChatGPT and then I let ChatGPT be the editor. This is the flow that I used a week ago. And then what I do is I take the result from Sonnet and I take the edited result from ChatGPT and I kind of play a little puzzle game where I just piece it together in a way that works for me. And that's how I do my writing these days. Now, the first draft would have already been usable usually even if I dump it down like I got. I don't know, with all the content creation, I have quite some experience With, I don't know, formalizing my thoughts. So the first draft is not bad already that I bring into Sonnet. That improves the flow, makes it better every time. And then I bring it into ChatGPT to edit. Now I bring it into 01 to edit. Okay, so we're gonna go step by step through this. But I just wanted to show you this one example why I will bring it into 01 mini every single time from here on out. No discussions. So the reason I will do that is the following. Okay, look, this example kind of shows. Well, this is going to be worth spending like 45 minutes of your time here with me on, because it's just better. Simple prompt. Okay, make this better, make this better. And then if you're not familiar, this is the first question in our FAQ on the Community sales page. This is the first question. I believe it's something like, why should I subscribe to Community if I can learn about AI for free online? It's one of the main points raised by people and I think it's a fantastic point. But I think we also have a fantastic answer. And the answer so far has been something along along these lines. Okay, so bear with me for a second here. Let's qualitatively review this. So the answer that we usually give is learning about AI for free is possible, but comes with challenge. With the challenge of every piece of content being isolated information that ends once the video article is over and the lack of thoughtful discussions surrounding all aspects of AI. Okay, so you know every piece of content, every YouTube video stands by itself and it doesn't allow you for like a nine hour long learning experience like we offer with the learning paths, for example. Right, so that's the first point. For example, the AI Advantage Community YouTube channel does provide quality content and then it goes into these points. Now you can have a look at this. I don't want to waste your time with like all the details here. I want to, I really want to focus on the first paragraph, the way this was written originally, the way I wrote this originally, or like me and the team in collaboration wrote this. Learning about AI for free is possible, but comes with the challenge of every piece of content being isolated information that ends once the video is over. And then there's here, there's a second point here. Now this is the main difference between 4.0 and 01. You can see it right here in the very first paragraph. Okay, again, identical prompt. This one is 01 mini, this one is 4O. Believe you can see it here. Yeah, 4.001 mini. Right? Okay, so what's the difference? First of all, it's more concise. Okay, fair enough. That doesn't mean a lot by itself. But then check this out. Learning about AI for free is definitely possible, but it often comes with the challenge of fragmented content. Each video or article stands alone, blah, blah, blah, blah, blah. Okay? So I want you to notice this, okay, Comes with the challenge of fragmented content. It's rewriting it, right? It's editing it. It's making it better. Why? Why does it say challenge of fragmented content here and over here? Learning AI is entirely possible, but it often presents challenges. Why does this. Any guesses in chat why this would say challenge singular and this says challenges and this is not anomaly. If I look at across all the generations in 4.0 challenge singular, this will be a. This was my long conversation, but it's the same thing. Challenge singular. So it's consistent, right? And then if I look across all 01 mini and 01 preview generations, challenges, challenges can look at more. But trust me on this, challenges. Every single time from the same prompt, it always points out that there's challenges. See that? So I'll give you the resolution here. The point is that I kind of pointed out. Well, not kind of. I pointed out multiple challenges here. And actually by learning AI just off the Internet, it's not going to be just one challenge, right? That's a complex problem. The Internet is a wide place. We're talking about human education. Like there's going to be more than one challenge, right? Maybe, you know, I should have come up with that when writing this original copy, but I didn't. And 4O also didn't. Because what 4O is doing, what 4.0 is doing, it's just generating token by token, it's going ahead. It creates this, then it creates this, then it creates this, then it creates this, and so on until it arrives here. And then it, you know, looks at all the context it has and it says, okay, I need to rewrite this comes with the challenge. Okay, let's rewrite it. But the main challenge, right, it does not consider the bigger picture. And it certainly doesn't iterate and think multiple times over the bigger picture. Whereas O1 and Owen Mini and O1 preview certainly do think about it. As you can see, this is not many steps. This one took a few seconds. I think using 01 Preview takes longer because it's slower. This took multiple steps, but every single time, as you could see across all the six generations, this is a really Important point. It considered the fact that there's actually multiple challenges. And this is a small example. I could nitpick a few other points. I could show you other examples that we'll do at the end of the lecture here. But the point is this. For all and all other language models, this is the main difference. They generate token by token, AKA word by word. This considers the full text and then thinks about it. It revises the entire passage. It navigates all AI educational challenges, right? It explores the scattered and brief nature of free AI content. Right? It explores it. It thinks about it. It's like a person sitting back in their armchair and being like, all right, so, you know, before I rewrite this, like, experienced writer, he wouldn't just go ahead and, like, rewrite it word by word, but he would think about it like, okay, what's the nature of AI education? What's the nature of education? How do people learn? Right? There's going to be more than one challenge, even though the original offer said there's one challenge and then later on goes on to list another one. There's multiple. And that's what's happening here. That's really the core at the essence of 01. And that's why it's better at rewriting. It doesn't just consider the full context better. It also kind of mulls it over and does all these extra steps to come up with the best copy possible. And that's why I don't think I'll ever be using 4.0 for rewriting or sonnet or anything else, because I want this. I want. And, you know, and arguably somebody could come in here, be like, Igor. But that's like. That's one word. Well, we could spend another hour here picking this apart. I think, you know, you could do that. I think this is superior copy to this. And how much better is it? Well, that's kind of hard to quantify, but it might mean the difference between somebody joining or not joining, and it might mean the difference between it actually influencing their life in a meaningful way. Depending on what you do with your copy, like, it could change. It could literally change lives. And I certainly, you know, I'm perfectionistic in certain ways, but I just have high quality standards. And I really, really value if something is better. And this is, to me, clearly better. And not just once, but consistently, because it thinks, okay, so that is sort of my first and overarching point. Keep that in mind as we go. For everything, this thinking process, there's value to it that goes deep, but not for everything. Okay, I'm highlighting this because this is a thing I do all the time. I use AI to edit all the time. Again, I write and then I rewrite with AI. ChatGPT is my favorite editor now. 01 mini is my best editor. Why is 01 mini better than 01 preview? Slightly? I don't know, it's just what I found from my testing. And also a lot of people online seem to share this opinion. Larry asking a question. Igor, do you have any custom instructions on 440? No, I don't. This is just the RAW output. I don't. So let's get into the little presentation I prepared here. Also, all the visuals in today's presentation courtesy of Microsoft Copilot. They're not great, but they're okay. It was super efficient to generate it with it. As I said, I'm working on my copilot skills. But either way, what you'll see today, it's all copilot generated. So there you go. Let me share my screen. All right, so yeah, I think what we started there with like that use case, it really illustrates it so well. It just thinks before it continues. And that's the overarching story here. Okay, so I prepared a little presentation that kind of summarizes things, gives you a few extra tools, a few ways to think about it. But then my goal today is going to be one, to actually fit it within the hour because a lot of people they scheduled for this hour. And two, to end this in an open ended manner that might inspire further conversation within the community. As you'll see in the end I kind of like raise a problem that you guys can grapple with on your own. But let us begin here. So what's the plan here? What are we going to do throughout the next 30 to 40 minutes? First of all, I'm going to give you a super quick overview just to catch everybody up. Then we're going to talk about limitations. Important topic, right? So many limitations with AI in the course we dedicate a chapter to it. We talk about this all the time. You need to be cognizant of these to get the most out of these models. Then we're going to talk about usage strategies. So you know, a few tips and tricks, a few platforms that I would recommend. There's one that allows file uploads today with 01 already. Also it should be noted, you know, this lecture is dated with October 1, 2024. So you know, at a later point in time, some of these things might change. As of today, this is all up to date, AI moves faster. And then we're going to talk about very, very rough prompting guidelines that I kind of came up with myself and in conversations with others. And then last but not least, we're going to talk real world applications and then you know, we could spend hours on that because there's a lot to discuss there, but there's some good ones. So let's get into the overview. Okay, so you know, very simple. This is a brand new family of models. It's not just a new model, it's a whole new like clothing line, so to say by OpenAI. It's a whole new brand. However you want to, you want to kind of whatever you want to compare this to. But yeah, these, these reasoning, these things step by step, chain of thought type of models. They're a new line and they're titled O1 and then probably consecutively O2, etc. Their models that engage in thought before responding. So I think I illustrated this quite well on the initial example. They think over the entire process. They think about the beginning, middle and the end. They do additional prompts, additional steps before they even start rewriting and use all of that in their context to get you better responses. Not always needed. The downside obviously is, you know, it's way, way slower. Some of these generations take 40 seconds, whereas with 4.0 it might take 2. Okay. The variety of different model models that we have today are 01 preview 01 mini, the faster and cheaper alternative and then the upcoming O1 that we should get throughout. You know, I don't know, maybe October, maybe later, who really knows. It employs chain of thought reasoning. We talked about this in previous prompt engineering lectures where we discussed various papers. Basically it's this concept of thought think step by step where it basically prompts itself a few times before it does the final prompt. That's kind of the summary. Welcome on board, Gerhardt. Good to have you here. And then it's trained on multilingual data sets and this is kind of an interesting reference point that we talked about in the office hours. Not sure this is too useful, but this came up in a conversation recently with also community member Rastislav Duzava and he, he kind of pointed out that. And I noticed this often in the thinking process. It starts it like thinks in Mandarin or in Hindi or in just foreign languages and you're like why would it do that? So we had a discussion about this and he also prompted GPT in various ways and he kind of came up with this theory that seems to be true that behind the O model it's not just English datasets that do this reasoning because a certain type of reasoning and certain type of thinking is better done in other languages. Let me just, you know, super small personal, you know, tangent here. I grew up trilingually and like a very unusual setup in my childhood. And certain languages, just like everybody who's at least bilingual will know that other languages, they just hide these, these specific cultural like gems and expressions and idioms that you just cannot translate. It's very hard and they're just unique to the language. And also if you kind of, for example, this is also super tangential but if you look at the history of psychotherapy it seems to be really heavily weighted towards German speaking languages, right? With a lot of the grades coming from Switzerland and Austria. And there's something about the German language that maybe, maybe it's, maybe it's in the culture, but I think a lot of it is also embedded in the language that just goes deeper than what you could express in other languages. Now certainly there's a lot to that discussion and we could discuss that forever. I just want to point out that in my experience and also seems like in OpenAI's testing and development using various languages and knowledge from various languages and writing from various languages seems to yield superior results when it comes to reasoning then purely going for English only data sets or maybe it's just a limited nature that hey, there's a lot of English data but if you include other data sets in other languages it just performs better. Whatever tangent over I just kind of wanted to point that out. And if you look at the reasoning steps, sometimes especially in the beginning you will it it way more often went into other languages and just the interesting kind of step side points there. All right, so what are the current limitations? Really quickly inside of ChatGPT, 50 messages per week for 01 preview and 50 per day for O1 mini. Quite simple. You do need the premium subscription teams or plus or Enterprise or Education those are the four to access it. The knowledge cutoff is in October 2023. The full feature set is expected at release with all the tools of the O1. Markus, yesterday in the office hour actually surfaced a little Screenshot from the OpenAI forum where one of the OpenAI employees actually confirmed that once OpenAI's O1 the full model, not the preview, not The Mini, when O1 is releasing, it will come with all the features. What are the features? I relisted them here for you. On the right side, their memory and Custom instructions, their data analysis, their file uploads, web browsing, their, the GPT integration and usage. They're the vision capabilities and they're the advanced voice mode. I'm very excited for this and I really think that giving all these tools to a model that can go that much further, giving it a bit more context and autonomy, is going to be a major step forward. A bigger step forward than the switch from like 4 to 4.0, for example. A bigger step forward than the addition of any single one of these tools. I think it's going to be a major jump and it should be very interesting here within the community as that happens. But I, you know, I don't usually talk about the rumor mill too much. We spent time on cold, hard, practical workflows. But some of you might know Mr. Strawberryman here from Twitter, the hype master behind this, this 01 release and before it was nicknamed, you know, Project Strawberry. Now he's back at it, tweeting again. I wanted to feature this one tweet here because I do agree with this point. The jump from 01 preview to 01 is similar to the jump we saw from GPT2 to 4. You'll be in a state of shock for months now. Okay, that might be a little bit overhyped there at the end, but then, who knows? At the end of the day, I think adding all of these, adding all of these functionalities to a model that does perform better is going to be incredible. Going to be able to upload, well, you're going to be able to upload all of your personal contacts, you're going to be able to give it screenshots of your website, you're going to be able to tell it, make this better and give it a random screenshot and it's just going to do its thing. That's quite incredible. And when the quality of doing that is going to be, I don't know, 20, 30% better than what we got with 4.0, well, that's a huge win because, let's be real, the quality of 4.0 very often and so, so, so many use cases that we look at here on this channel and Community, all those use cases, they're like slightly below the threshold of where they become like incredibly amazing and you want to use them often. They' like, this is really good, but it's not quite there. Right? That's kind of the feeling as I would describe it. In so many cases, if we get a 20, 30% boost and if this holds true, then I think we're going to be at a very interesting point in time in Generative AI because yeah, as I said, I think a bulk of the use cases that we explore and prompt for, they're just below this threshold of being useful and just this little boost in ability and in quality might just tip it over. And then, yeah, I think even more mass adoption would be the logical consequence of that. And then, yeah, everybody in this room will be not just one, but multiple steps ahead because we're dealing with all this stuff already and then that's good. So if you use it for the API right now it's only available to users that are tier 4 above. What does tier 4 mean? Well, this is a screenshot of the OpenAI website. You can see if you spend $250 or more in the API then you have it. I believe this is throughout the last month. Certainly tier five was. So you had to spend thousand dollars or more in the last 30 days and then you had a one on release. Right now they put it down to tier four. If you don't use the API a lot then you're not going to have it. That's kind of the gist of it. Okay, and these are kind of the final numbers. I'm not. You can check this out if you want. That's the pricing. It's the most expensive model OpenAI has. It's one of the most expensive mod. It's pretty much only opus. It's a bit more expensive. It's really interesting to check out this little calculator over here. I'm just gonna head on over and show you the website here. But this is a nifty little tool that we've also been using in yesterday's office hours. Not sponsored or anything, it's just a cool little tool and that's completely freely available. And you can go in here and say something like, okay, if I have you know, 800 tokens of input, which is like pretty common, and it outputs 500 tokens and I make. I was looking at a project where I hook up a form and to my YouTube audience recommend various free resources that we have. Usually, you know, I don't know, my estimation would be maybe we're going to like require 8,000, maybe 10,000 API calls for that. So I can put that in there. The inputs tokens would actually be a lot because like the prompts that I crafted there, they include a lot of context and multi shot examples. So it would be something like this. Output tokens would be something like this would be my Estimation. And then here I can quickly check how much it would cost with the various models. If you check it out. 01 preview right here. This little fun project would cost me $3,300 in API costs. Not so great. If I go with The Mini, it's just 670. Way better. If I go with four. Oh, Mini, it's $33. Ha. Incredible. And actually the new king in this category is Gemini 1.5 Flash as updated last week, $3.36. This is how cheaply you can actually generate 10,000 custom emails with a 20,000 token input, which is a lot, as you know. So this is an incredible price. Is the quality good enough for writing emails? Probably not. My preference here, qualitatively would be Sonnet coming in at a juicy $690. So just a good calculator, just a good tool to bookmark the presentation. And all the links will be in the event recording as per usual. So no worries about that. I just wanted to show you this. Thought this was useful. Okay, back to business. Let's get to the interesting part here. Okay, let me skip over these. We were right here. So the interesting part. How do you use this? How do we get more out of this? What have I found? What have I and the team found so far? Well, first of all, you can use it via ChatGPT. Plus, this is kind of the traditional way. You're only going to have 50 messages per week. So very quickly I ran into that limit, especially when I was testing certain things and rerunning 013 times, right? Like test one prompt three times, another prompt three times, another, another, another, and all of a sudden you're like up. You're done for the week, not for the day, for the week. Right. 01 preview. Luckily for things like rewriting and things that are not related to the STEM fields, Mini is actually better. There you have 50 per day, but that's the initial way. Secondly, if you're level four or higher, which most people are probably, probably not, then you can access it through the API and use the playground with it as. As per usual. But here's my tip. If you want to go beyond this, if you hit this limit and you want to go further, poll is your friend. I tested various tools. Multiple community members actually recommended various tools. I know various people are using a lot of these new platforms that have popped up, platforms that allow you to, um. Basically you pay a monthly subscription and then you get. And they do, they do the API calls for you and you get like a chat interface. There's many of these. But the one, the one that I found to be actually the best with 01. And I say the best carefully because that's. That's quite the strong term. But I think it's objectively the best is poll. I have to pull up pole. Where did I place my pole? Going to reopen poll, like so. All right. Why is PO the best? It's the only one that has working file upload. Okay. And also like voice recording. But the file upload is the main thing. You can actually upload PDFs to this and it works with it. Many other of these platforms that I've tested offer file upload. But then with Zero1, concretely, with Zero1 Preview, it just doesn't work because it's costly. Right? Like if you upload something, they have to vectorize it, they have to store that. They have to put all those tokens into the context window and then also pay for it all. So they'd rather disable it.01 preview on PO actually works. I could add a file here, run a prompt here. It will work. This does require subscription. I believe it's 25, $26 a month or something. But I'm. I am not entirely positive how many. Okay. Okay, There you go. You have. You have a million tokens per month. One message of 01 is 10,000. So here you're getting. You're getting twice the Messages as in ChatGPT. I suppose if you're maxing it out on a weekly basis, you're actually getting half, right? Because in ChatGPT you would get 50 times, times 4, 200 messages. Here you get. Here you get 100 messages in total. So, you know, but you get them right away and you can use them right away. So if you're kind of stuck, I would recommend this. I know Dirk also saying here, specializes in that some time ago. And I know also Dirk is a big fan of PO in general because it allows you to, as so many people in the community are, because it allows you to use all of these various language models with some more advanced features and fun fact. And I'll just kind of throw that in before we move on. The creator of po, the CEO of po, is also the creator of Quora, that you might be familiar with a big online forum for answering questions. And I believe his name is the Angelo Cora. What is his full name? Adam d'. Angelo. Yes. And Adam d' Angelo here, if you didn't know, also anybody who's. Who has been following the OpenAI drama will know that Adam D' Angelo was and also still is on the board of OpenAI, as you can see here. So, fun fact, this popular chatbot platform that ships features right before OpenAI regularly. Okay, this is not the first time they have these additional features always before OpenAI happens to sit on the board of OpenAI. Now, my little conspiracy theory when I put my little tinfoil hat on here would be that they actually use this as a testing ground before they ship it into ChatGPT. And this has been the case multiple times now. I don't know, just kind of wanted to point you towards that po Great platform. You can use all of these different, all of these different chatbots. And now let's move on. If my Mac allows me to. Yes, it does. Thank you, Mac. Okay, so let's get back to a little presentation here. PO might be the way to go if you want to go beyond the limits. You know, there's a way to pay for more tokens even if you don't have the, if you don't have the API access. All right, so few prompting guidelines and then let's get into practical use cases, shall we? So these are very rough. Okay, like, this is definitely not like, hey, this is not law. But first things first, the one thing that is clear and that is law actually is what OpenAI recommend they don't release. Don't tell it to think step by step, step by step. The entire thing is trained and fine tuned for step by step thinking. If you tell it that, you're just going to make it worse. Secondly, as with all prompting markdown formatting and clear delimiters really help. So if you use certain formatting conventions that we talked about many times in the community, especially in the advanced prompt engineering learning path, we go into how to structure your prompts, how to create bullet points, sub bullet points, how to use H1, H2, H3 headings for the various parts of your prompts, it still works best because it's the Internet. It's, it's the Internet way of communicating hierarchy. That's what it comes down to. And that just helps. And my last point here is it favors short to medium length context rather than long context prompts. Matter of fact, if you give it super long prompts with five short examples, it often just completely disregards them because in the thinking process it kind of takes on a life of its own, which is kind of creepy, to be honest. You know, it just, it rethinks your input and it's like, okay, human, thank you for those instructions. But you know what, like prompt number seven suggested that we should do it differently. So I'll just go, go with that. And all of a sudden it just disregards all your initial instructions and does its own thing. Yeah. So that's why longer instructions are not recommended. But they certainly work. I feel like what I've, from what I've seen on the online discussions, people have condemned it. Like, oh, one doesn't work with long instructions anymore. Prompt engineering is that. I think a lot of that is actually just theatrics or like just is there to, to engage people and to enrage people. Like, oh my God, now I've, you know, like, oh, all my prompts are going to be useless or something. It's just there to elicit the emotional response. It's not actually really factual. I have some examples here that will go into next slide is basically like use cases and we'll go one by one and look at some. But I have some examples where I gave it some of our most complex prompts that we teach within the community and the courses and it performed really well on them. In some cases it just kind of improved it in a way where it thought it would be even better and just went off that and then disregarded kind of the templates that we gave it in the five shot prompts and all the detailed instructions. But it certainly works with long prompts. I would not, never, never would I state like not a single one of the complex prompts that I tried, like didn't work. They all worked. They maybe didn't work exactly as they would in 4.0. It came up with its own ideas of how to interpret all that, but it's aware of all the instructions. It's very similar, if not identical language model in the background. The outputs are similar to 4.0. Right. It just does this extra thinking in between. So I wouldn't say long prompts don't work, but I will say that it works exceptionally well with short prompts. Even if you just give it something like, but not too short. Right. So if I just told it and my other examples, remember how I told it, make improve this text, that would be short by itself. But I give it a lot of context with the text. Right. Then I give it the text and all of a sudden it's like a medium sized prompt because there's like two free paragraphs of text. And that's what I found to work best. If you just tell it like, hey, I want to create a business plan, good luck. Like that doesn't work. Well, even if for my company, AI Advantage, that doesn't work so well. It works way better if you kind of like go towards a short to medium area where you tell a bit about the business plan, but then you also tell it about the company and you tell it about your goal with the business and then you let it do its thing. So don't keep it too short. Right. Remember like when last year we had, we covered some of these auto GPT, excuse me, and baby AGI and these different frameworks that kind of popped up. These are just first agentic frameworks that were publicly available and there the prompting was really like just state the goal and like hands off and let it do it, let it do its thing. That's not the case here. It actually works better if you tell it the goal, but then also flesh the context out a little bit. You know, add that extra sentence, those extra two sentences, the target audience flesh out the goal a little bit. That's just, you know, just my personal take. Come up with your own ideas. I'll be sharing things as I learn them, but this is what seems to work best for me and all the other people that I've consulted this with. By the way, this was such a fun journey over the last two weeks. Sorry. For anybody who I harassed with the question of like, and what are you using O1 for every team meeting, every office hours, I kind of brought this up, got a lot of interesting answers. And this, this presentation is sort of the culmination of that. But yeah, it's been fun. I really enjoy doing these innovations events. Once a month we get to kind of like dive deep into a specific topic. And the topic that we're diving deep into today is OpenAI01 real world applications. That's what we're here for. We arrived, we have about 15 minutes left. And what we're going to do in those 15 minutes is I'm going to show you various prompts that I ran for both for all and 01 and 01 mini. And then I'm going to kind of comment on the results and on why you would want to consider some of these and why this is probably the best model for the things that are listed on screen right now. So let's just briefly go over them and then get into the concrete examples. I don't have concrete examples for every single one of them because we don't have time for all that. But for multiple here I do. So first of all, superior for content editing. This is the example that I showed off right off the bat. That was where you give it the text and you just want it to rewrite. You're not using it for creative writing, you're using it for editing. Now, I did include creative writing here, too. As you can see, bullet point number three, but especially storytelling. This is one that has been raised by Daniel Pearce, team member. He said, it's really good at storytelling, and I can confirm. And we had a little conversation about this. And it's the same idea of it kind of abstracts away from your requirement of the story. And it thinks about, okay, what makes a good story? How should we structure it? What other elements could we include in the story to improve it? If it does all that, it's going to craft a better story for you. It's going to consider the beginning, the.