 Hey Chris, you hear about that OpenAI GPT store? I sure did Greg, yes. So like, is it as awesome as everyone is saying? I think so, yeah. It's pretty cool. Oh nice, nice. So does that mean it's the answer for everybody, consumers to enterprise alike? I wouldn't say everybody. No, no, not close. Like even the enterprise edition. Maybe that one's the answer, right? In some cases, definitely, but definitely not all or most. So it depends. Something like that? Strong, it depends. Yeah, okay, okay. Well, today, let's see if we can demystify and break through all of the hype, get to the bottom of what really matters, and what folks trying to be AI business leaders today can do to create some value for themselves, for their companies and the world. Sound like a plan? Let's go. Let's do it. All right, we'll see you back in a bit, Chris, for our first demo. Thanks for taking the time to join us for today's event. Fresh off the back of OpenAI's Dev Day last Monday. My name is Greg Loughnane and I'm the founder and CEO of AI Makerspace. Today, you'll learn about the tooling and processes you need to be thinking about as you and your team set out to build generative AI applications from 2023 into 2024. We have a special guest today, Evan Shai, CEO of Coding Temple, who we're excited to partner with today, multi-time founder, built some great companies, and he'll be joining us for the Q&A portion of today's event. And of course, we have the LLM wizard and CTO of AI Makerspace, Chris, who we just saw and we'll have back on the stage soon. So with that, we're going to get right into it today. We're going to talk about how to build your competitive AI advantage today in the crazy, maybe the craziest market anybody's ever seen. At AI Makerspace or AIM, we like to align our aim every time we set out to teach anything. And by the end of the day today, you're going to really understand, hopefully truly grok some of the tooling and the processes that you need from low code and even no code to a little bit of code to develop the proof of concept and MVP level apps you want. And then also when we think about building your own GPT or building chat GPT for your private data, what are the actual steps you need to go through to get where you're trying to go with this? And what are some of the things you should think about both before and after you start playing with the tools all right first off to all the business leaders out there i know you're all asking yourself today like how do i judge the new latest and greatest tool the auto magical platform that somebody's always trying to sell me. Nowhere is this more obvious than the GPT store that was released since last week. And so we're going to dive in. We're going to try to figure that out. And this idea of how to build chat GPT for your data coming up nearly on the one year anniversary of chat GPT. This is a big question every company is asking themselves. And hopefully we're going to really get to the bottom of what we need to do to make this happen and how we can think about creating value for our products and our customers by the end of today. First, though, some context before we get into prototyping with some of this new tooling and the process you need to think about when you actually go and build these prototypes, when you actually start empowering your team to get after it with your own private data. All right. To contextualize everything first, I mean, I know the entire world is focused on LLMs and so is this particular presentation. Generative AI is all the rage, but it's important to remind everyone that the value in AI today that's been created to date, these are the solid circles inside of this chart here, most of the value is not in generative AI. Most of the value exists in classical ML and classical deep learning, in something called supervised learning. Now, you can just think about that as sort of the original machine learning and the companies that have built really, really powerful systems. Many of them leverage this simple tool that's going to continue to grow in the next three years. This is the more faded circle around each of the solid circles here. more faded circle around each of the solid circles here. However, generative AI is going to grow many times its size as it's doing in real time in front of us. And this is where a lot of the focus is going to be for companies heading into the new year, just because of how much buzz surrounds this entire idea of building a GPT for yourself. But again, this is where most of the value has been. Most of the value many think will continue to be in the supervised learning domain. These are potentially easier problems, but they don't use as but they don't use as new or as interesting of tool sets. Stepping back even further, if you're a digital product company, the important thing to ask is, how do I improve my product for my customers? Not how do I use AI? And so at the end of the day, most applications that you've set out to build to improve your customer experience, they still don't need AI. And of the ones that do need AI, most of them still do not need generative AI. Okay. That's just my caveat of the day here. But of course, every board, every CEO, every business leader wants to be up to speed with the latest and greatest. And of course, every board, every CEO, every business leader wants to be up to speed with the latest and greatest. And of course, that's generative AI and LLMs. And when you start building with generative AI and with LLMs, the line between prototype and production becomes even finer than before. We used to call it going beyond the Jupyter notebook, and we tried to make sure that the models we built in a notebook environment also performed the same in production. This was not as hard as it is to sort of guarantee the output of a large language model application. model application. Once you ship it to production and the large language model starts engaging with your customers and stakeholders, it's not always clear exactly how it's going to behave in all situations. And this is the problem of evaluation. How do we know something that comes out that is generated character by character, token by token, or word by word from a large language model is right or is good or is high quality. These are often slippery and ambiguous terms. And so we need to be a little bit more careful before we put these products in front of customers at scale. We need to prototype more quickly and get to production more quickly, but for the early adopters among our customers. And we need to dial in what evaluation actually means so we can retrain our models in the right amount of time and with the right data. And we can continue to make sure that that customer experience is improving and never falling off of a cliff because of some crazy outputs that our models might provide that we didn't see coming. And this idea of AI transformation as we deploy more and more advanced models to production, it's really important as an AI business leader that you kind of go all in on this. There are, of course, no promises of massive upside. You're going to make hundreds of millions of dollars and capture the entire market. But it's more that if you don't start investigating these tools and you don't start adopting them, then you might be opening yourself up to massive downside. And the thought leadership that's going to be required to keep up with this state of the art. I hear from CTOs all the time at AI Makerspace. This is the fastest they've ever seen anything evolving or maturing their entire careers. And that pretty much says it all. If you want to stay out there on the edge, it's going to be harder than ever before because it's going faster than ever before. So if you want to be an AI business leader, best practice, make sure you go all in on that AI transformation at your company. Now, if you decide that's something that's for you, then the first place, the unfortunate place where this always starts is you need to get buy-in from the top. And unfortunately, the truth is that it always starts here. Well, AI is existentially scary to our competitive future. If we don't do this, competitor A, competitor B, competitor C, they might eat our lunch, take our entire business. And so the worst strategy is to just not start at all. Okay, now they're listening. Now what? Well, now we demonstrate this idea of the AI transformation flywheel. Hey boss, listen up because this is what we're going to aim at. We want to develop our giant industry leading competitive advantage flywheel that's based on our data and the models that will continue to improve. We need to build this flywheel out of smaller wheels. We need to get started with pilot projects. We need to identify the right things, the lowest hanging fruit to spend time on. We need to get those into production, start seeing some small wins with our customers. We need to expand the idea of how we did this out to other folks within our company. And of course, if we need to, we'll grow the team through hiring. But hiring is an often misused strategy in a marketplace where the only way to keep up is to be constantly learning literally every week of your life outside of work. So this consulting and training and hiring, it's going to be some combination of these. There is no one silver bullet and somebody's got to lead the hub in the center. And if that hub in the center is going to follow along with best practices of other companies in the space, you're probably going to develop a place where people can come to talk about why they would do a specific AI project, about what they should be building with, about the tooling and processes and team structure they should be building with, about the tooling and processes and team structure they should be thinking about in their organization or functional department, what they need to do to train their folks or to even understand themselves what's going on. And that internal consultancy has to be led by someone, the generative AI business leader. That's going to be the seed through which you can grow the AI DNA into your company. What does this look like practically? Well, there's two functions that may not exist in your company yet. One is AI product management, and the other is AI engineering. AI engineering is a broad term that encompasses all of prompt engineering and data science and everything sort of applicable on the edge of what matters today. Aligning stakeholders down to the classical engineering data science teams is the task of these two groups. And of course, the AI product management function is not going to be built out first. It will be the AI engineering function under a suitably motivated AI business leader. So that's going to be you and the small AI engineering team that you start to build these pilot projects with. So that's the idea is you want to oversee this hub. You want to develop those pilot projects with. So that's the idea is you want to oversee this hub. You want to develop those pilot projects of lowest hanging fruit. You want to always be focused on your customers, your products, not on AI first, but on your customers and products first, then on AI. And of course, you're going to have to be the local influencer, the evangelist, and the place where people can go to find answers. And you won't have them a lot of times. But if they come to you, you just say, I don't know yet, but I'm going to find out and get back to you. One quick success story for people to think about as they think about their product suite, their product line. Airbnb has a great example because Airbnb is not in the AI business. They give you a place to stay anywhere in the world. And sometimes when you go other places in the world, you have to speak other languages, but a lot of people don't. And so rather than trying to get people to learn new languages, they can leverage AI to make communication between guests and hosts easier than ever. Because state-of-the-art translation is a tool that is ready for prime time. When you think about your particular problem that you're trying to solve for customers, for stakeholders, think about how AI can augment that problem and make a better solution. Again, don't start with AI, start with your context. So why isn't AI adopted yet? Well, part of the reason is because these teams that we talked about haven't been built out yet. And it's hard to figure out how to build these teams with the minimally sufficient people within a market and an economy that's seen increased interest rates, have seen big layoffs, have seen an overall desire to do more with less. Building out massive teams doesn't make any more sense than it used to. And it's always been something that's been a bit hard for most companies to do anyways. This is the long tail problem of actually rolling out solutions at scale. We've seen companies like Facebook, like Google, capture entire markets for things like web search, and advertisements by building out these huge teams. The entire long tail of small and medium sized businesses are never going to be able to build out these huge teams. They've been waiting for the low code no code to a revolution. And it's coming. It's here. If you know where to look, and you can be one of these early adopter companies if you're attending this event that's probably of interest to you by knowing what to pick up and what to start building with today you see it's not about the large language models those are going to continue to improve and it's not really about the killer application side either the general models are going to continue to improve. And it's not really about the killer application side either. The general models are going to get better generally. The killer apps are going to get better in a low-code, no-code way as we've seen, like with OpenAz GPT store. The killer app is just a chat. It's just texting like you would text your mom, your sister, your brother. It's just meeting the user where they are with a text-like interface that adds value to your business and to their engagement with your products. That's where we can start today. We can worry about additional killer apps in the future. We can worry about all of this other stuff in the future. Even the idea of a hyper local AI model trained on all of your proprietary data. We don't need to boil the ocean to get started today. Although that may very well be where this heads. Rather, we can get started with prototyping. And the first place to start, of course, Rather, we can get started with prototyping. And the first place to start, of course, is with GPTs. This is starting to be kind of a hard acronym to track these days, as many jokes have been made about since last week. But when we're talking about GPTs, we're talking about OpenAI's recent low-code platform that you can actually build your own custom GPTs on. GPTs lets you customize ChatGPT for a specific purpose, for your purpose. They say the best GPTs will be invented by the community. Also an interesting thing to think about as you think about your own internal data science consultancy in your business. That's the community you want to build. Privacy and safety were definitely top of mind as they have been for so many people building these systems. Connecting GPTs to the real world, aka augmenting them with search and other tooling, making them more agentic, so to speak, is a way to actually improve some of the ways our users could interact with these models. Then, of course, everybody wants to deploy their internal only GPTs. It's important to note here two things. One is that we found this little checkbox on here as we were building this week and preparing this for you today, where you can opt in to use conversational data in your GPT to improve OpenAI's model. And we went ahead and clicked the checkbox on this for our little app today. And more generally, one of the things we're always reminding folks that we train to be AI engineers and we're training folks to really come into this game is the same thing that Sam Altman reminded everybody during OpenAI's Dev Day is they do not train on the data that goes in and out of GPT 3.5 Turbo or 4 or 4.5 Turbo now through the API or through chat GPT enterprise, which they've recently rolled out. They're saying they do not train on this data. That doesn't mean they don't have the data to look at. It doesn't mean they don't assess the data for whatever they might want to understand about their users. It just means they simply do not train any of their models based on that data. So one quick and easy way, if you've got sort of in-between data, that's not super important to be private, personally identifiable information, health records, et cetera, is to just use the API instead. And while we will show you how to do that today, what we're first going to do is we're going to show you how to build a no-code GPT with the absolute latest and greatest tool from OpenAI. And we're going to do it using some Coding Temple data. Partnering with Coding Temple today, we found some public, rich content that they have on their site and you probably have on yours as well and this is often the easiest place to start to start getting buy-in from folks at the top and so to see exactly what we did with this rich blog content and building our own gpt i'm going to send it over to the llm wizard himself chris alexiuk who's going to send it over to the LLM wizard himself, Chris Oleksiak, who's going to walk us through it. Chris? Thank you, Greg. Yes. So the idea is today we're going to build something just like this, which is a custom coding temple GPT. It's going to allow us to have the ability to query some of their documents as well as, you know, inform the tone and style that this a particular gpt is going to respond in so we have a sample question here how could i combat imposter syndrome and then we get some information this information is fairly you know straightforwardly from their blog so it's good information sense, and it's grounded by that rich content that Greg was talking about. So the idea is, you know, how do we make this thing? Well, with the new GPT builder, we can go to my GPTs, we can click on create a GPT, and then we can build one. GPT and then we can build one. You'll notice that we have this message GPT builder section, which we can do things like say, hey, you know, can you name my GPT coding temple GPT? And then we can send that message off. The idea is that we can interact with this tool in a absolutely no code way. We can interact with it in a completely conversational way, and it's going to be able to update our settings. It's going to be able to change what our actual application looks like. And as you can see, it's going to suggest things to do next like it's currently generating a profile picture it's going to do that with you know it's basically going to be able to do that using dolly which is amazing now if you want a more kind of traditional experience we can also do the configure option the configure option is going to help us to, you know, just interact with this like we normally would. But you'll see here that we've generated this sweet picture, which looks like a temple of coding. And we have this coding temple GPT rename, and it's changed everything we need. We can lock this in and say, yes, that looks great. Now let's head over to our configure option. So you can see that it's updated the name. We could add ourselves a description. And we can also go, you know, go forward and give it some instructions, right? So maybe we can say something like, you are a professional. Professional, you always answer politely and courteously. But if I could spell though, right? So, oh man, I really, really botched that one. But this is the idea. We can give it instructions. Let's give it something silly. And you speak like a pirate. We can add conversation starters. So things that our users can click on to begin their conversation. So we can say things like, you know, how would I combat imposter syndrome with a or in a in a programming, you know, role. Right. So now this is a programming role, right? So now this is a conversation starter. We can upload files. So just like traditional RAG, we can upload files. So I'm going to go ahead and upload a HTML document that I got from the actual blog from Coding Temple. We can upload this as a PDF, and then we can have it parse the PDF while we're interacting with the system and give us answers relevant to that PDF. We can give it the ability to reach out to the web or if we prefer not to, we can shut that off. We can let it generate images and we can let it create and run code if we needed to. You can see we can upload a bunch of different files if we wanted to. But the idea is that we have the ability to build our own retrieval augmented generation system without ever touching a line of code. You know, if we go to our additional settings as well, we can opt out of the setting our data so that they don't use it for training, which is very nice. We can even add custom APIs here that we've created or others have created to really elevate our system to the next level. And then we can look at a preview, right? So we have this Coding Temple GPT. We can click on this preview, and it's going to reach into those source documents for us. It's going to decide which context it's going to use and then it's going to give us an answer that's relevant. And as you can see, because of our instruction, we have the classic example where it's going to speak like a pirate. Obviously, this is just a toy example. But the idea is that we can shape how our system interacts with people we can shape what resources it's going to leverage and we can do all this without ever writing a single line of code once we have this done we can go ahead and save this and we can share it to others so they can use our custom gpt um it's just a really powerful system that's really good at you know letting you prototype extremely quickly regardless of your your programming or coding experience. So, you know, very powerful tool. And that is, you know, your custom GPT feature from OpenAI. Super powerful, super awesome. And with that, we'll head it back to Greg. All right. Yeah, Chris. Looks like it does what it says on the 10. Very cool. I guess that's a wrap, right? Well, actually, what we just saw was that tool, that no-code tool is just a wrapper for the OpenAI model. It's a no-code solution to create a simple RAG or Retrieval Augmented Generation system with just one document there. We just used one blog. And it's great to get something up and going, to maybe show the idea, to get users or stakeholders engaging with it. But where do you go from here? Where do you go if you say, I want to take this and I want to use it for my business? I want to use it for the enterprise. Well, let's think about why we might not want to use OpenAI's GPT tooling. Well, one fun example from this past week that has been getting some traction online is, well, maybe the data is not as secure as people thought it might be. Here's an example from the co-founder of levels.fyi, which is all about tracking salaries, stock vesting schedules, and other information about folks getting jobs. And the conclusion here is maybe it's best to treat any data you upload to OpenAI to be as good as public. I think that's a conservative view of the world especially if you're actual competitive advantage remember it's not in the models anymore it's in the data so levels levels has ostensibly the best data on levels um if you lose the data you might lose your advantage. And shout out to Antony here who dug in and found he was able to access the data from level FYI's GPT, which included those salaries, those vesting schedules, and even gender information. So this definitely highlights one thing you might want to avoid. And one of the things on every business leader's list of potential trouble they don't want to get into is data leaks. But what about other stuff? Well, you know, think about the fact that there's been no engineering whatsoever done. There's been no integration into existing products. There's been no engineering whatsoever done. There's been no integration into existing products. There's been no instrumentation of how to actually collect data on usage or on anything else. And of course, if you send this to somebody, what you're essentially asking them to do is today, you're asking them to pay for an OpenAI Plus account so that they can even engage with your GPT. They're not paying you then, they're paying OpenAI. And you probably saw that Chris did this ahead of time, so we didn't see it in real time. It's because it's not the fastest tool right now to kind of do this type of work and it's hard to exactly understand how you might go about really really speeding it up so when you start to talk about okay now i've got my poc what do i do from here to iterate on it well one of the things you want to do is you want to actually go iterate on it well one of the things you want to do is want to actually go probably build an app that you can do in a low code way but that gives you the extensibility that gpt level apps do not there's a big difference between extensible low code and no code in this game today. So let's back up to first principles. If we're going to build LLM applications, generally we want to start off by prompting as much as we can. How far can we get with prompt engineering? From there, if we need the model to understand additional data it wasn't trained on. One way to take a cut at this is to build a question answering system, AKA a retrieval augmented generation system. One that essentially leverages your data. Whether we're talking about question answering, whether we talk about augmenting generation, or whether we talk about chat GPT for your data, we're talking about the same thing. A chat interface that takes into account stuff that is important to your user and your context. When it comes to prompting, the top, top rule is write super clear and specific instructions you probably saw in chris's demo he was giving instructions to the model so it knew how to engage with the user but that context is oh so important this is that you are an expert in coaching people who want to grow their online presence, let's say in the imposter syndrome example. Or even if you give an LLM something like you are very hungry or tired or angry today, then it's going to have a very different output and result. This context could also include that data of yours. Of course, you can provide examples, one shot, two shot, few shot learning. You can provide thought through and reasoned examples through chain of thought reasoning approaches. And you can ask for outputs in specific formats to really get your prompts dialed in. You can go very far with simply prompt engineering. To take it to the next level, to do question answering over your own personal documents, to do RAG, we want to understand why we would want to do this in the first place. Well, LLMs, when you just prompt engineer them, they oftentimes will lie to you. They'll say things that aren't true. This is a problem I noticed when I was playing with the GPT model we put together as well. It said, well, I actually don't have any information on that in my source documents, but here's an answer anyways. Okay. Okay. All right. This confidence in false answers, this is the false positive that AI practitioners are so familiar with. How do we get around this? Well, in business as in life, as an AI, we need to make this fact checkable. We need to make sure that we have references to things that we're saying are the answer in our applications. When the LLM is able to retrieve references, find those references in the source documents, augment the prompt with those references, adding those bad boys directly into the input prompt, then we get improved generations, improved answers, a better experience for our customers, and places where they can go to find even more information. This is called retrieval augmented generation. Retrieval, finding the references, augmenting the prompt with those references, and improving the answer as a result. When you have very specialized domains with really specialized words and jargon, there's even more that you can do here when building these RAG systems. But it's really obvious that if you have really specialized words you use, you might want to start by putting those into your application a little bit, and you can start directly by putting them in with your PDF level data using a RAG setup. So just to give you a quick overview of this, because we're going to go from no code to low code here. There's three easy pieces to understand to retrieval we ask a question we have an input we search our database of stuff that's similar to the question and we return stuff that's similar the way this looks is we take our documents like our little blogs we chunk them up into little pieces, pass them through what's called an embedding model that takes the tokenized text and actually creates word embeddings or vectors from those chunks. And then we store those or a vector store or an index these are all synonyms once we have all of that set up we can ask a question what's the deal with imposter syndrome and we can get some similar reference material out of our data for coding. We've got a blog for that. So the way that this looks is, you might ask what should AI business leaders know to succeed these days? We're gonna pass that through an embedding model, turns it into a vector. We're gonna go search our vector database. We're gonna set up a prompt template. We're gonna do a little bit of coding here, but it's in natural language. So it should be okay, even for business leaders to understand we say use the provided context to answer the users query this is our instruction you may not answer the users query unless there is a specific context in the following text if you do not know the answer or cannot answer, please respond with, I don't know. Very important. So we take our similar source material, our references, and we pass those bad boys directly in to the prompt. We stuff them in to the prompt window, the context window. in to the prompt window, the context window, we shove all of that into our LLM or GPT, if you will, and we get our answer with source material. Now this is a rag system in general, but for boots on the ground, if you're looking to go start building, yeah, see how far you can get with prompt engineering. Yes. See what you can do with the OpenAI GPT store. But always start with synthetic data and closed source models as you're getting going with your POCs all the way up to your MVPs. If you can get away with similar data, closed source models, you can build faster. Okay. Speed is paramount. Once you have it set up with synthetic data and closed source models, move to open source models. Once you're able to do that additional extra lift of open source models, now go ahead and add your private data because now you're not concerned about anybody seeing it. Baseline your performance there. Is it solving the customer problem? Is it actually adding value? Then from there, you can reduce the model sizes. You can do fine tuning on embedding and large language models. You can keep iterating, collecting user data, improving, doing the LLM operations that are going to be so important for your long-term success. Some companies have even more to keep in mind. For instance, are you in the space industry? So you need on-prem hardware. You can't even use the cloud. What scale and speed do you really want to be rolling this out at to start with? Is it 10 people? Is it 100 people? Is it 1,000 people? What kind of speed do you really want to be rolling this out at to start with? Is it 10 people? Is it 100 people? Is it 1000 people? What kind of performance will you need? How quickly will users require they can interact with that? What will that look like at scale? Truly at scale? Does that mean that you're going to need to train smaller models so that you're not hosting these big mega models to solve this simple downstream task? What's the process for going about fine tuning your way down to that sort of solution? The one that can be deployed at scale? What does that actually look like? Well, that's a topic for another day. For today, we're going to take it from no code to low code. And we're going to take it from no code to low code, and we're going to show you that if we add a little bit more code, we can add a little bit more data, making it a little bit more performant and a little bit easier. So we're going to welcome Chris back up on the stage now to show you how to think about this from the AI business leader level, and also how to give you a little bit of material that you can get your budding, aspiring AI engineers kicked off with today. Chris, back to you, man. Hey, yeah, thanks, Greg. So basically, the idea is we're going to use this Chainlit application here to chat with our PDF we're gonna pass in that same PDF that we uploaded to use our uh you know our custom rag that we built with uh GPT so our custom GPT if you want to call it that and you know we could ask the same kinds of questions uh And, you know, we can ask the same kinds of questions. We can ask things like about business use cases, right? So we have a kind of question queued up here, which is this about, you know, AI business leaders, right? So what does an AI business leader need to know about building internal training programs? You can see that we're using this chain right now. We have this question, this chat chat history using the stuff documents chain we're going to provide a bunch of different sources and then we get this response right now busy at least know several things and this is all information that's derived from the blog uh written by the people at Coding Temple. So it's a really powerful tool to, you know, kind of move us into the next phase. Now, the idea here is that, you know, we have the ability to create this tool in a kind of much lower code fashion. We can, you know, see here that we get our sources back, which is great. And if we look at the actual code, you'll see that there's only 150 lines of code. Most of this is just boilerplate and prompts, right? And the idea is that's all we need to create this system. And the really key thing about this is we have full control over the same system template, but also how we're splitting our documents up. Also what kinds of documents we're setting, accepting, right? We have things like, you know, what kinds of embeddings we want to use, right? How we handle memory. All of these things are now at our fingertips, whereas before, you know, we would have had to do some, either a lot of different changes in prompting, or we'd have to just abandon the GPT tool, right? Since it doesn't handle the specific use case. So the idea here is that this is a simple thing that we can build on top of. And one of the first things you might think of is like, well, it's OK. You know, it's great that we can do this one PDF at a time thing. But what if we wanted to use the whole blog system, right? Well, this is where, you know, we can extend our application. So we're going to drop this collab link into the note, into the chat here. And the idea is that this is going to help you use a combination of both open AI and cohere in order to let you, you know, scrape all of the blogs from coding temple, chunk all of them blogs from Coding Temple, chunk all of them, and then combine them into some documents and then do some rag on top of those documents. You'll notice that we're going to use a more powerful embeddings model. So this is the Cohere embeddings v3 model, which is currently at the top of the embeddings benchmark, right? It's even beating out things like ADA. So GPT's models, it's beating out a ton of different embeddings models. And we're also using this re-ranking pipeline, which is going to basically cast a very wide net of contexts and then use a special model to re-rank those contexts into the most relevant pieces of context. So it allows us to do this kind of inefficient search on top of an extremely efficient search, but on a small subset of the results. Very powerful tool. And with this tool, you'll see that we can, after some setup, we can ask questions like, what are some of the ways to avoid imposter syndrome, right? And to avoid it, we can engage with supportive online communities, seek feedback, we get great advice, basically. We can see the sources that we use to get this advice, including some blogs that are about, you know, becoming your comprehensive six head uh you know this is a guide uh you know discover how coding in public helps battle uh imposter syndrome we have a bunch of different sources that we're pulling from here all from the coding temple blogs to get this response and so because we have all of those blogs instead instead of just one or two blogs, we can also ask the same question that we asked our single PDF application and get a answer that relies on that information. So the idea here is that we can extend these systems beautifully once we have them in a kind of code-focused format. And this isn't a ton of lines of code still right so we're we're really we're really able to take these systems from kind of that v0 the custom gpt and extend them all the way up to much more performative systems very straightforwardly using these tools like langchain, Cohere, and other APIs to help us get the job done. So that's the idea of what we're talking about today, right? You can prototype super quickly with those GPT style models, and then you can really hit the paint hard and get your kind of like lower code, simple solution, and then extend that into this kind of more performative system as you as you evolve and your team, you know, takes on new challenges. So we'll, we'll head back to Greg. But that's, that's how we do it in a in a more, more code focused solution. And you can take this code and send it to your engineering team, if you want and have them, you know, figure out what they'd like to do with it. And there you go. Yeah. Awesome, Chris. The extensibility of the Lego blocks are so important as we start building. I love it. And, you know, the takeaway from today is really that there is low hanging fruit out there, but you got to start with your products. You got to start with your customers. You got to get that And, you know, the takeaway from today is really that there is low hanging fruit out there. But you've got to start with your products. You've got to start with your customers. You've got to get that flywheel going. It's easier than ever to get it going once you know what you're trying to build and why. That's where prompt engineering comes in. You pick up a quick GPT. Build it. Fake data, remember. Synthetic data first. Closed source models first, start understanding how to go to a low-code solution. You can take our chat with your PDF, you can duplicate it directly on Hugging Face. There are many other similar applications like that on the platform. You can build with OpenAI and then an open source embeddings model, then you can build with an open source model, and then you're completely open source, and you don't need to worry about anybody messing with your data. As you get into RAG++ or ChatGPT++, you're no longer concerned that your data is being used to develop this thing, which is ultimately where you need to be. to develop this thing, which is ultimately where you need to be. So with that, I'd love to welcome Evan Shai and Chris back up on the stage for the Q&A portion of today's event. So if you're sticking around, you still have questions, definitely go ahead and copy this Slido link, hit the URL and upload your questions. We'll be sure to get to as many as we can. Guys, thanks for joining me. Thank you gentlemen. Thanks Greg. Thanks Chris for both the intro and partnership and great presentation. I'm certainly thrilled to see so many people joining us here today, explore the frontiers of AI and business, if you will. I know we share the belief that developing AI capabilities internally is no longer optional, but imperative to remain competitive. That's kind of what we focus on at Coding Temple is technical skills broadly. We help individuals and organizations develop these critical technical skills and end up training a little over a thousand professionals every year in data analytics, cybersecurity, software engineering, and increasingly through partnerships like this AI. And what I have noticed is that there's just a lot of uncertainty, right, in terms of navigating this complexity, where exactly to start next. So partnering with AI experts like yourselves to help business leaders navigate that complexity, I think is critically important. And thanks for kind of shepherding us along that journey today. And I'm excited to dive into some questions. Yeah, awesome, Evan. Well, yeah, I think we've got a good one at the top here. Let me push it right back to you, man. With the rise of AI, what skills should our workforce be developing to try to stay ahead? If I'm a business leader, how should I be thinking about this problem? As you can imagine, this is something we think about, you know, often, obviously, and I think both upskilling and reskilling talent is critical. I think professionals, generally speaking, you know, should be focusing on developing AI literacy, and then maybe kind of understanding data ecosystems and how data plays a role in that process. And then just really the ability to work alongside AI, including the ability to question and refine AI outputs ultimately. I think at the individual level, and you touched on a lot of it through the presentation, start playing around with these tools immediately. I mean, they're pretty accessible. They serve as kind of a nice on-ramp, if you will, to start to intuitively think about, how can I make myself more and more efficient in work? I think when you start talking about kind of organizational wide leadership, it can be a bit more complex and actually because now you're talking about a set of skills and capabilities, not just data and AI, but also organizational strategy, talent, and culture, I think it's critical for organizations to bring help in from the outside to help, again, kind of navigate this constantly evolving space and experience workshops like this and conduct kind of deeper audits and analyses in order to see where the best opportunities are and where can we create a kind of a steady stream of training as well as talent to scale those AI capabilities. Yeah. Yeah. Yeah. You know, I just feel like there's a sort of like maturing idea of AI engineering that's coming into focus that we sort of see from AI makerspace. I mean, if you had to sort of articulate that, Chris, like, what does that look like in terms of folks that people have working for them today? Like, who should they be looking to to become the AI engineer of the future in their organization? They're looking at software engineers, they're looking at data scientists, security engineers, like prompt engineers. What's the deal with all of these titles? Yeah, yeah i mean there's a lot of titles for sure uh the the ones that i think ai is most in critical need of if we wanted to talk about you know uh uh what what roles uh you know are missing is like traditional software engineer backgrounds are going to do very well uh you know adapting to this ai workflow it is a lot of uh you know that traditional software experience that really helps you uh you know exploit or or really you know squeeze the most juice out of these ai systems uh but there's a place for all of the roles you mentioned i mean we need security is a a very important task with LLM applications. Data science exists and is even more necessary than it used to be in terms of guiding our direction. Where should we take these systems? How do we improve them? How do our users feel or experience our tools? And so it's, I mean, everyone has a home, but I think the most critical role right now to upskill is those classic SWEs to really get them leveraging these tools and kind of launch them into the stratosphere. Classic S-dubs. I love it. Okay. So next question from Islam. Do you think this GPT builder will possibly affect negatively what happens to people who are trying to pursue careers in ML, like building rag systems or computer vision. Anybody have a hot take on this? Chris, what do you think? Definitely not. Zero percent shot. It is a really good tool, but we have had really good tools our whole lives. And the thing that we have done every time our field, whatever version of software you're in gets a new tool, you know, in JavaScript and all these web tools came out. I mean, they didn't kill any jobs. They just moved where the talent spent their focus. And I think, you know, getting up to that higher level of abstraction and getting into a flow where, like, you can use these tools, leverage these tools to accelerate your productivity and your business's productivity. I can't imagine we're going to run out of use cases for these kinds of tools. And because of that, I think if you're looking to get into this field, it's still the best time ever to get into it. I mean, it probably will continue being the best time ever until we really design something that just takes us over so it's uh i feel pretty pretty strongly that this is uh not gonna kill anybody who's pursuing a career yeah so evan when somebody new comes to coding temple and they're sort of worried about the latest and greatest and you know they're looking at this sort of basic sort of foundational stuff they need to learn but they're sort of like but shouldn't i be uh focusing all my time on this the shiny object that just came out uh what do you what are you telling them yeah i think i would echo probably some of what chris just said i think you know these tools are going to continue to evolve and they serve as really useful on-ramps to expose you to the capabilities of what these technologies are able to do. I think still, especially as you want to kind of press down the capability spectrum, if you will, where real innovation and competitive advantage can happen, I think you still require some of these more durable foundational skill sets and understanding in order to be able to really lean in and create some advantages there. So we, practically speaking, are kind of constantly updating and evolving how we teach and train on these tools. But the kind of first principles, the foundational elements, I think are as critical as anything and will remain durable and provide utility to professional trying to get into the space. Yeah, yeah. And it's so interesting because even as you go up the chain within the company, it's important to know about that new new. And it's like the board wants to hear about it. They want us to be on top of it. They want to be able to tell their stakeholders that, yep, yep, we got it. We we know what we're doing and so if you can be somebody in that company that sort of you have to build a foundation but also is reaching out and understanding those on-ramps at the edge you know you're going to be and you're communicating that internally you're going to be you know forced to contend with within a company going through a transformation. So yeah, I think it just adds, adds a little something, you know, gets you quicker on the low code app game, you know, and keep, keep going there. So, you know, the next question, we've got an ROI question here, Evan, I'm going to kick this to you. So you can kind of take this, however you'd like here. What should businesses consider when investing in AI? If they're trying to maximize their return on investment today, what do you think? Yeah, it probably depends on where you are on the product life cycle to some degree. But I think, you know, when you do think about ROI of AI investments, I think you can push it beyond just kind of cost savings into value creation. I think that's where some interesting and exciting things happen, you know, whether it's enhanced customer experiences or new product capabilities, so long as those capabilities are delivering better customer experiences and customer value. I think, you know, that is where I would increasingly focus. I think in the short term, there are some natural like low-hanging fruit efficiencies that can be captured measured and articulated sufficiently to drive kind of buy-in and understanding within the organization which might help you get the resources you require to go after some of those longer tail value opportunities but if you think about the kind of long-term growth metrics and strategic objectives, you know, expanded LTV, brand equity, customer loyalty and retention, all of those types of things I think become interesting. Yeah. Yeah. It's such a bottomless question here. Yeah. It's almost like the answer is everything, you know, maybe consider everything a little bit. Okay. In the interest of time here, I want to see if we can get there a couple more this one Chris what do you think Islam will you be creating any videos for the RADI T paper that was released recently maybe you can tell us a little bit about this should we sure yes I mean so it's a raditz is pretty cool. It's you know, it's similar to the work that was done by the intent rag. So the original kind of rag pipeline, which involved that intent training it's, it's not, it's not nearly the same, but it's in a similar vein where we're thinking about these systems and how we fine tune them together. So it's definitely a cool and interesting thing. It's you know, it will probably gain more adoption as people continue to ask the question, should I fine tune RAG? And the answer is, well, yes. You know, there's situations where you love to do that. Okay, all right, got it. So maybe we'll name drop it Islam. I don't know if it deserves its own event or not. I'm not convinced yet, but all right, very cool. Last, we're gonna close it up with what are the most exciting developments in AI that you believe businesses should be aware of? Of course, GBT store covered it today. Obviously you're trying to get you guys up to speed. Evan, what do you think? What are the things are you paying attention to today that you'd encourage all the business leaders out there to keep their eye on? Yeah, again, I kind of just alluded to it. I think, you know, oftentimes we talk about the efficiencies that can be captured creating content. It's a legal document or marketing content. I would look kind of beyond that into what kind of new insights can this technology unlock for myself and our organization. So, you know, the ability to take in our own data safely and discover new customer value opportunities is certainly something I get excited about. It's something that you say require not only specialized tasks, but like a series of laborious specialized tasks to analyze and synthesize, right? So, that is something that we spend a lot of time thinking about and how we're going to ultimately leverage it. I would encourage other business owners and leaders to be thinking about it similarly. Yeah, yeah. And I guess like my two cents is kind of like start collecting data today that you think might be useful for tomorrow. You know, if you don't, and start collecting it in a way that you'll be able to use it later. most important data is the data you collect tomorrow there's another way of viewing the world and even if you view it that way set yourself up for that chris any thoughts on exciting ai developments that you're paying attention to there's just so many greg uh some of the the coolest stuff is how these optimization or inference optimization kind of tools that we're gaining access to as we move through the space, which lets you run these bigger models on smaller hardware or faster or better. I mean, there is so much in that ecosystem right now, from a serving and inference side, that I think is worth paying attention to, especially for businesses that are kind of locked out of these closed source or API based solutions, right? Every flop that you save or every, you know, GPU minute that you save is, is $1 back in your pocket. So I think it's worth paying attention to those services. Love it. Paying attention to scale before you need it. With that, we're going to go ahead and thank you so much, Evan. Thank you, Chris, for the Q&A. We're ready to wrap up everybody. Thank you for your participation today. This brings us to the end of today's event, brought to you by AM Makers you for your participation today. This brings us to the end of today's event brought to you by AM Makerspace and Coding Temple. If this session resonated with you as a business leader, you might consider one of our tailored corporate training packages built on top of our upcoming LLM Ops, LLMs, and Production production cohort, where aspiring AI engineers learn everything they need to know to build, deploy, and scale production RAG applications. Tailored training includes pilot project ideation, one-on-one coaching and mentoring throughout the cohort, post-cohort individual project consulting, and an internal demo day hosted directly by yours truly and you, business leader, for your executive leadership team. So if this is interesting, please go ahead and book with our team, and we'll be happy to have a chat with you, see if it's a fit. Otherwise, we'd love to hear your feedback on today's event and how we might bring even more value to you next time we get together and do some teaching and learning at the edge. Feedback surveys in the chat and until next time, keep building, shipping and sharing and we'll do the same. Thanks, everybody. See you soon.
OpenAI GPTs: Building Your First No-Code GPT
GPT-4 Summary: "Unlock AI Leadership and Innovation: Join Our Groundbreaking Event! As we approach 2024, the era of Generative AI thought leaders and business operators is here. This event is a game-changer for those ready to lead AI transformations in their organizations. Learn practical frameworks to identify high-impact AI use cases, develop rapid MVPs, and effectively guide your teams in building these MVPs with a focus on speed and iteration. We'll move beyond the hype, offering real-world examples, success stories, and access to open-source low-code and no-code tools. Perfect for aspiring AI thought leaders aiming to drive technical and financial success in their businesses. Be part of this transformative event and start reshaping your company and teams with the power of AI today!"

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Speakers: 
Dr. Greg Loughnane, Founder & CEO AI Makerspace.
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Chris Alexiuk, CTO AI Makerspace.
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Evan Shy, Chief Executive Officer, Coding Temple
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