Explore AI Literacy for IT, HR, and Legal Teams

0:00Hello and welcome to the first skill in the AI Compliance Privacy and Copyright for HR, Legal, and IT course. Before we talk about policies, privacy rules, or copyright, we need to understand what AI actually is and how it evolved. And we'll do that in this course, which is part of a series of AI mini courses. And we are here. But it is worth mentioning that technical teams can take a very similar course here in AI Compliance Privacy and Copyright for developers. And most of these mini courses include a micro crash course on prompt engineering.

0:40And this is the mini course on prompt engineering that I highly recommend that you take if you want to learn more about prompt engineering. Here we go into much more detail on prompt engineering, but it's still a mini course, which means that it's not a full course. And if you do want a full course, please let us know in the comments, because this is precisely the data that we need to hear in order to make the content that you are most interested in. And for those of you who are interested in programming, but don't have a programming background, AI vibe coding and security is a great place to start. Essentially, it's like prompt engineering, but for non programmers who want to leverage AI for programming with a special emphasis on security, because if you can't read the code, you might want to be careful. And there are steps that we can take to mitigate the dangers when vibe coding, which is incredibly fun. And if you do have a programming background, then I would suggest the AI genetic coding course. And finally, if you're building an application from scratch or you're part of a leadership team, then I would suggest the artificial intelligence for executives and leaders course. And finally, these two are AI productivity courses. The first one down here,

1:55AI productivity for professionals is a platform agnostic approach to AI productivity. But really, we're going to lean very heavily on Google in this course, because there's really just two choices when you're talking about these large platforms that have self-contained ecosystems of applications. There's Google that uses Gemini and then Workspace. And then you have this one, AI Essentials with Microsoft Copilot, meaning we're going to use the Microsoft ecosystem with Copilot and Microsoft 365. So you can choose A or B. And the only other choice is to use different AI chatbots and connect different parts of these ecosystems into them. That is not a best practice for many reasons, particularly when we're talking about AI compliance, privacy and security, which you're going to learn about in this course. And on that note, what's included in this course? So what is in scope? We'll identify the difference between main types of AI, because it's not just one thing.

3:01There are many different things and have a really cool way to break this down in a non-technical way because it can get very complicated very fast. And if we think of it in basic terms, then you can build a mental model that you'll not only use for the rest of this course, but also outside of work as AI becomes more and more ubiquitous in the workplace. We'll also understand basic non-technical, that's the most important part, AI compliance, privacy and copyright. This is the non-technical course for HR, legal and non-technical IT teams. We'll also leverage AI tools using prompt engineering for basic governance. But it's also important to mention, this is not a full course on AI governance because that is a much more in-depth course. That would not be a mini course, that would be a full course. But what we're doing here is a mini course, sort of like a crash course on the most important parts of AI governance distilled into the key topics of compliance, privacy and security. And don't worry because we're not going to learn how to code. This is a non-technical course and no tutorials on how to build AI chatbots or AI agents, which is a lot of fun, but we're not doing that in this course. But first a little bit about me. My name is Jonathan

4:21Barrios and I'm excited that you're here because I love to teach data science, machine learning and deep learning. And in this course, that really means AI, but really what I call myself is an AI data scientist. It's a combination of data science and AI engineering, which includes deep understanding of both machine learning and deep learning. What you're going to learn about in this course. I've worked with many different types of AI built SAS applications, AI applications and worked on streaming platforms. And more importantly, I've been teaching for quite some time at top platforms like Treehouse, Thinkful and Chegg. And now I'm happy to be part of the CBT Nuggets team. So where can you find me? You can definitely find me on LinkedIn at Jonathan dash Barrios dash AI or on my blog website at Jonathan Barrios dot com. And you can also find me here on X at AI underscore data underscore science. And again, that's what I call myself.

5:23That's the field that I'm in because it's an emerging field and it's also my X handle. So a quick note about X. Some people love it. Some people hate it. But to be quite honest, I don't really engage in social media because I think it's a waste of time. So you might be wondering, why are you telling me to follow you on X? And here's why. The only reason I'm there is to keep up with all of the developments in the AI community. And the AI community is mostly on X. It's incredible. For example, top minds like Andrej Karpathy are continuously posting amazing things directly on X. And it's really the simplest way to get information about the AI community.

6:06Plus, you can use a built in AI grok, which is part of X AI, which is integrated into X. And that's all I do. I only follow people who talk about AI and I do not follow anybody else. That way, my feed is really amazing. It's kind of a superpower, really. So follow people like me and others that talk about AI and you'll be in a great spot. And otherwise, I highly recommend not using social media. It's not great. And instead, use that time to learn.

6:38All right. Now, here is the first skill. We're going to identify the main types of AI, because like I said, AI is not one thing. And before we talk about policies, privacy rules, or copyright, we really need to understand what AI actually is and how it evolved. The truth is, AI didn't appear recently after some new breakthrough. It's the result of decades of change in how computers learn. And instead of a technical explanation about the various types of AI, I have a very simple way to approach this. And I like to call it the path of least resistance, meaning it's the easiest way to achieve a goal. And I'm borrowing this term from physics. And while this can mean a lot of different things, it just means we're building a mental model to understand AI so that we can leverage it to better understand AI governance. And that's it.

7:34And the way that I've chosen to do this is by breaking down AI into its simplest components, meaning we'll look at the evolution through the lens of three types of software. Software 1.0, which is traditional programming. Software 2.0, where the software learns from data on its own. And software 3.0, which is the generative era. And this is what we know as chat GPT. And that's what I mean that AI is not one thing. A lot of people think it's just chat GPT, but that's just one slice of the pie. And by the end of this skill, you'll see exactly how this evolution affects today's bigger compliance questions from protecting employee data to managing bias in hiring or ensuring copyright safe content generation. So in this first skill, we're really laying the groundwork for everything that follows. We'll explore what AI really means, not from a coding standpoint, but from a business and compliance perspective, you'll learn to spot whether an AI tool your team uses is rule-based, software 1.0, data-driven, software 2.0, or generative like chat GPT, which is software 3.0. And this is a much simpler way to understand AI.

8:55And each type is associated with very different risks and responsibility. For example, when we're talking about HR, systems using data-driven AI may accidentally learn hiring bias. Legal teams using generative AI tools may face copyright or confidentiality issues. And when considering IT, these departments deploying AI chatbots may expose private information if prompts aren't handled carefully. Once you recognize the type of AI behind the tool, you'll know what kind of safeguards or policies that it needs. So that means we're going to define what AI is in plain language, explore software 1.0, 2.0, and 3.0, and understand traditional learning and generative systems.

9:45Then we'll connect it to HR, legal, and IT examples. And finally, we'll identify early compliance and privacy challenges. We'll do all of this to bridge to the next skill, on-prompt engineering. That's where we're going to apply prompting for safe and compliant outputs. Meaning, when you understand how AI learns, you can better manage how it behaves.

0:00When people hear AI, they often think of tools like ChatGPT or Copilot or other models or systems that can write or summarize text, which is also known as generative AI or Gen AI for short. But like I said earlier, AI is much broader. At its core, AI is simply software that learns from experience. Instead of following strict rules, it identifies patterns in data and uses them to make predictions or generate ideas. Even when it's generating something like text in ChatGPT or Copilot, it's predicting the next word. And this is how it generates those ideas.

0:42So let's break it down into three easy categories and also demystify what artificial intelligence is. You can really think of it as an umbrella term, which could be many things like rules, search, machine learning, deep learning, and generative AI. But we're really going to focus on the core technologies that represents AI, and that would be machine learning, which finds patterns in the data. For example, HR systems might predict which candidates could be a good fit based on past hires. And then a subset of machine learning is deep learning. Here we use layered models to understand complex data like images or speech. In short, we're using software modeled after the brain. So deep learning uses something called neural networks, which are artificial neurons that mimic human neurons. We have neurons in our brains that are connected, which means our brains are made up of neural networks. And that's how we think, talk, store memories, and so on. And that's what makes deep learning so fascinating. You can think of machine learning as algorithms and deep learning as these neural networks. For example, in IT,

1:59you could have security tools that recognize phishing emails or facial authentication on devices. We'll explore each one of these in more detail soon. And a subset of deep learning is generative AI. Here, gen AI creates new text, images, or even code based on prompts. An example would be legal teams using AI to summarize contracts or draft policy templates. The takeaway here is that gen AI is just one slice. It's not the whole pizza. And what we're doing is simplifying this because we don't need to know all of the learning styles like supervised, self-supervised, unsupervised, reinforcement learning. These are all really interesting aspects of AI, but we don't really need to know that. And when we're thinking about modalities, we can use this simple mental model to see how it's used with vision, speech, and text. NLP is just the technology, natural language processing that we use for text and also recommendation. For example, when you're watching a YouTube video at the very end, it gives you recommendations and even robotics. Here's the key concept. AI doesn't know anything. What's most likely based on its training data. If the data

3:16includes bias, private information, or copyrighted content, the AI may reproduce those in its output. And that's why compliance isn't just about setting rules. It's about understanding how the tool learns and how to use it responsibly. And when we look at AI use cases in everyday life, you can see it in your phones when you use it as a way to authenticate who you are so that it opens up your phone. That's facial recognition and that is deep learning. So it's classifying you specifically, but it can also be used for other applications, identifying individuals in a crowd, and so on.

3:57Computer vision also uses deep learning and here it's classifying a person, a car, a tree, a sign, and so forth. And this is how computer vision allows cars to drive themselves in the city streets. And when you're thinking about robotics, well, that's also deep learning because it uses computer vision to see. But how about if it speaks? It could be a combination of deep learning plus gen AI. How about these little motors that are in the joints? Maybe those use machine learning.

4:29The takeaway here is that all of these can combine. It doesn't mean that one AI type is for one thing. When you're thinking about fraud detection, machine learning is an algorithmic approach to identifying outliers, such as very large transactions in a completely different country. And that's used for fraud detection. And when you think about intelligent document processing, IDP, you can take a picture of a document and convert that to text. That would be deep learning.

5:00But when you use that document to generate new text, well, that would be gen AI. And again, we're combining these technologies to create different products. You could use deep learning by itself, machine learning by itself, or generative AI by itself, or you can combine them. And here is where we build our mental model. A much simpler way of thinking about AI and all of the technologies contained in that umbrella term, such as machine learning, deep learning, and generative AI, is to really think of it as three types of software that evolved pretty quickly.

5:37Software 1.0 is traditional programming. If you've heard about coding or programming, that's software 1.0. Software 2.0 is where machine learning and deep learning come in, because those models learn from data. We're not using rules to program them. And then finally, 3.0 is generative AI. These are models that generate something new from its training data. And using this mental model allows us to think more clearly about AI compliance, privacy, and security, instead of being bombarded with all of these technical terms that can be complex.

6:16While they are interesting to learn about, I think it really doesn't help to get too deep into those technologies, unless you want to build the models yourself. If you are somebody in HR, or a legal team, or you're on a non-technical IT team, it's much better to think of it in terms of software, how they behave, so that you understand how you can apply compliance, privacy, and security to your organization for your department or team. And in the next video, we'll begin by discussing software 1.0, how rules are created by hand, meaning code creates behavior.

6:55See you there.

0:00Welcome back. In this video, we're going to explore software 1.0, 2.0, and 3.0. We already know what artificial intelligence is, more or less an umbrella term for machine learning, deep learning, and generative AI. We know how they're implemented in everyday use cases, such as computer vision, robotics, and facial recognition. And to manage AI well, it helps to see how software has evolved, because every new era changed what humans are responsible for.

0:32And that's why we're doing this comparison. When you consider software 1.0, these are the rules that we wrote, meaning we wrote rules by hand. And by hand, I mean code. So programming that creates some kind of behavior. You can think of early HR systems that only followed exact filters that you created. For example, when looking at potential candidates, you might look at years of experience, and you might set a criteria that you want that to be greater or equal to three years. In this case, the privacy risk was low because nothing was learned or stored beyond what you entered. If you use this explicit rule, greater than or equal to three, it's not going to change magically, unless there's an error in the code. But once it starts to work, you'll be able to rely on that check for the years of experience. And that's why the risk was low.

1:23And when we look at software 2.0, things are a little bit different, because now systems began learning patterns from examples. For instance, analyzing past hires to predict future performance. And so you might imagine, what's the challenge here? If your data has any bias, your system is going to learn that bias. And this is an important key point. Responsibility shifted from writing the code to curating clean and fair data. So here, the code in software 1.0 is really responsible for the behavior. But here in software 2.0, we are training the model, also known as fine tuning, that creates the behavior. But really, this is data quality. If there's bias in that data, well, then your system is going to learn that bias. So the responsibility is to make sure that that data is clean and fair to begin with. And when we look at software 3.0, these are rules that are created using prompts.

2:24You can use tools like ChatGPT to summarize this confidential report or draft a job description. This might save time, but it also raises new questions. Could private data leak through prompts? And that's a great question we're going to explore in this course. Who owns the AI-generated output? How do we ensure the response is accurate and compliant? So what we're doing when we're entering prompts, we're steering the model to create the behavior. And when I think of prompts, I really think of programming in plain language. Instead of using programming for software 1.0 or using data for software 2.0, we're using plain language to steer that model to create that output. It's like creating a program using plain language. And this is the framing from Andrej Karpathy, who also said English is the hottest new programming language. And again, we're talking about software 3.0. These are the chatbots like ChatGPT, Copilot, Gemini, Grok, and others. And while each stage brought huge productivity gains, they also shifted the meaning of responsible use.

3:40And that's why we have this course, because it is counterintuitive where the responsibility lies, mostly because we don't understand how these models are trained. And that's what we're going to explore in this skill. We're going to use this hamburger and this hot dog as examples to train a model. We're not going to write any code. It's going to be fun. And it's easy to understand how we're training a software 2.0 model. And if our data, which are images of the hamburger or the hot dog have bias, then the model is going to have bias. For example, if I use 100 images of hamburger and one of hot dog, guess what? It's going to favor hamburger. And that's pretty easy to understand. And it's also a lot of fun, like I said. And this is going to give you the insight that you need when you're thinking about compliance, privacy and security. You can simply think to yourself, well, is this a program? Is this traditional software programming where we enter some data? The rules won't change until we change the code. And in that case, well, the responsibility lies with the person that is doing the programming. And when we think of software 2.0, when we're using these different labeled images where the model learns from the data, the responsibility shifts to the person creating the data for the training. And to make

5:01it simpler, we can just say the person that is gathering the data and training the model is the person responsible for the fairness and the behavior of the output of that model. And when we get to software 3.0, well, that's going to be prompts. So the responsibility shifts to the person working with prompts. And that is precisely why we're going to get into prompt engineering in the next skill, because that is how you can make your outputs compliant and mitigate any privacy leaks or data leaks or security vulnerabilities. Now let's take a look at an example of software 1.0.

5:39We can say that this is pre AI, meaning computers didn't learn anything. They just followed the exact instructions written by humans. So the humans write these rules by hand, meaning they create the code. And that's what creates the behavior. Every decision had to be hard coded. If this, then that. So let's take a look at what this looks like in Google Colab. The reason that we're using this is again, you're not going to learn how to code, but this is probably the easiest way to get started with any programming example without having to do any setup. And all you need is a Gmail account. That way I can just type some code in here and then run it and you can see how it behaves. So for this short Google Colab example, we'll build a simple HR rule-based system, the kind of logic early recruiting software might have used to screen applicants. Here's how that works.

6:36So first I'm going to define some kind of criteria. So applicant years equals four, and we can easily change this number. So for right now, I'm going to click on this play button, which runs the code, and it's not going to do anything. It's just going to save the number four so that we know what the applicant years are. So in this case, the applicant years are four. Now we can do some sort of hard coded rule. If applicant years is greater than or equal to three, then I want you to print this statement. And here we can say something like candidate meets experience requirement, but we need to also handle if that is not the case. So we can use something like this else print this. And here we're going to say candidate rejected, not enough experience. All right. So let's run this code. Okay. Candidate meets experience requirement. Perfect. What happens if I run this again? I get the same exact output. If I do it again, the same and again. So it is deterministic. So here's what's happening. The system checks the applicant years of experience up here, which is four. And it does that here. If the applicant years are greater than three, print this statement. If not, then print the statement. If X, then Y there's no flexibility,

7:56no learning and no hidden logic, which is good and bad. So to recap software 1.0, everything is deterministic, meaning it's going to be the same predictable output every time. Once you have this logic set in place, unless something happens to this code or it's changed, then it's always going to be predictable in the same. That makes auditing and compliance much easier because you can trace every outcome to some kind of rule. And here's the rule print this else print this pretty clear.

8:29If the rule was biased, like filtering out applicants without a degree, every result would reflect that bias. If the laws changed or the job criteria evolved, the software wouldn't adapt until a human rewrote the code. So while software 1.0 carried low privacy risk, it offered no adaptability. Everything depended on human logic. The next video, we'll take a look at software 2.0 and get the hamburger and hot dog into this classifier so that you can understand how we train these models and how easy it is to introduce bias when you don't have quality data.

9:12See you there.

0:00Welcome back. Now that we've seen how software 1.0 followed explicit rules, let's move on to software 2.0. Here, the world of machine learning and deep learning. Sometimes when you hear machine learning, deep learning is the subset of machine learning, but they are sometimes also used interchangeably. But the easiest way to think about this is algorithms, some kind of sequence of steps. And in here we're using neural networks. But to keep it super simple, you can just say that all of this is machine learning.

0:33It's not super important to know the distinction between machine learning, which generally uses algorithms and deep learning, which specifically uses neural networks. But it is super interesting because chat GPT does seem to work like a human in some cases. And that comes from deep learning and the artificial neural networks, which is really software modeled after the brain or software modeled after us. So software 2.0 really is the world of machine learning, which includes deep learning.

1:04That means that we stopped hard coding logic that was software 1.0. And instead we show examples and the systems can learn its own rules from the data. And to bring this to life, we're going to use something called Teachable Machine, which is an educational application by Google. And we are going to step away from the H.R.I.T. and legal examples and take a look at the hot dog and the hamburger as training data. And in doing so, you'll have a firm understanding of how software 2.0 works.

1:39We show it these examples and then it learns to make predictions. And then we can talk about bias very easily and hallucinations, meaning when the model just starts to make stuff up. And that has to do with the examples that we're showing them. This is very important. And that's why we're going to do this example. And as I promised, it's going to be fun. All right. So here we are at Teachable Machine. It says here, train a computer to recognize your own images, sounds and poses. A fast and easy way to create machine learning models for your site, apps and more.

2:13No expertise or coding required. So let's click on get started. And here we have a choice. We can do image project, audio project or pose project. In essence, these are all the same thing. Computer vision. Here we're giving it images. So that's pretty straightforward. Here we're turning the audio or sound into a visual representation of sound. So again, images. And then finally poses. These are just sequences of images, lots of images. It takes all the images from your webcam and stitches them together to create the training data.

2:47So much larger project. So let's start here with the image project and we're going to use standard image model here. And now we have two classes, some kind of a training. And then here we preview, meaning this is where we test the prediction accuracy of the model. So I'm going to go over here and click hot dog. That's our first class. And then here, hamburger for the second class. And the way that this works is pretty straightforward. You click on the webcam and then click on crop here. And then let's move this into an area where I am not. I can move out of the way a little bit and then that way I can grab a hot dog.

3:21And there we go. There's the happiest hot dog on the Internet. So we can just hold this down and we want to get, let's say, about 100 samples. OK, we have 103 and to delete them, I'm just going to hit the trash can by hovering these images. And now we have 100 samples of hot dog. Let's do the same thing for hamburger. OK, I'm going to crop this, get out of the way, and now we can use the hamburger. So what would happen if we just did very few of these, like three? I don't know. Let's try it out. So now we're going to train the model. And this is the part where it learns from the data.

3:54It's looking at all the different patterns. So now it's ready for predictions. I'm going to click on crop again, move this out of the way so that I'm not confusing the model. And now we can hold up a hot dog and it gets that right. How about hamburger? It's a little bit confused, especially if you do something like this. Oh, it thinks it's a hot dog. Oh, that's definitely a hamburger. Now it's a hot dog, right? It's a little bit confused. And that's how we introduce bias, because we only have three samples here. So let's go back to the webcam and let's do this. Let's add some more images to fix that.

4:25OK, 101. Let me take away one of these. Perfect. But if I go over here and I try to make a prediction, it's still a little confused because we need to train the model again. All right. It's done training and now we can test it out. Hamburger 100 percent. No problem this time. And if I take it away, it thinks that the background is a hamburger. And if I get into the picture, it thinks I'm a hamburger, which I'm not. So let's click on add a class and now let's add background or we can say waiting or let's say background. And all I have to do here is crop, get out of the way and hold this down for 100 images.

5:01I can delete one of these. And if I go back into the sample, it thinks I'm a hamburger still. So I need to train again. And now we have three classes. This is pretty common because you don't want to classify the background is either a hot dog or a hamburger. But this is a way to show you how this works. Not saying background. But the minute I do this hamburger and then the minute that I do this, we now have hot dog. And if I bring a phone into the picture, it has no idea what's going on because this is not fair.

5:31It's like I don't even know what this is. This is a phone. Same thing with coffee mug. It's really confused. And all that means is that we haven't given it the training data on different samples. We only have hot dog and hamburger and now background. Because if I move out of the way, it's background. If I get into the picture, I'm a hamburger. Well, not really. And that's exactly how chat GPT in these 3.0 software models can make mistakes. They're built using software 2.0 through this process.

6:02But instead of images, we're giving them large amounts of text. So imagine we're giving it a lot of text in English, but then no other language. So these chatbots would be biased towards English. And that's why fairness is such a huge component for software 3.0 and software 2.0. Because training the model means selecting the right data and making sure that the corpus or the body of text that you're feeding into these language models is representative.

6:35All languages, all kinds of knowledge. And as you can imagine, that can be pretty difficult. All right. So let's break this down. In software 2.0, we taught a tiny classifier, meaning it was doing two classes, hamburger and hot dog. The background was just a placeholder for the educational example. But software 2.0 doesn't mean that it has to be just two. It could be many different classes. It could be 10 types of food or 100 types of food. So the main distinction between software 1.0, which are rules that are hand coded through a programming language, here we're using rules that are in weights.

7:11So weights just means how important are the different features. For example, when you look at a picture of a hot dog, it's looking at all of these lines. So it has the mustard and ketchup here, the two little eyes, the smile. These are all features. And it's asking itself, what are the most important features in this hot dog? Even when we move it upside down and all around. And the same thing goes with hamburger. Here we're looking at all of these different patterns and shapes. And it's saying what's important in order to identify each class, hot dog and hamburger.

7:47And it's not important that you understand weights so much, but you do now understand training because we did that. We fed it all the images and then it trained. And that means it collected patterns. And as you can imagine, certain patterns are going to be more important than others. So the importance are weights. So how important is a shape like this when you're trying to identify a hot dog? Very important. How about a shape like this? Not so important because we don't see that shape inside of a hot dog. How important is this shape in a hot dog?

8:18Not so important because we didn't really see that. So this one is going to get a higher weight and this is going to get a lower weight. And that's how that works. So instead of hard coding weights, the model learns patterns through training. Fine-tuning is just a specialized version of training where you train on a specific set of images. But you can really just consider both of these as training on examples that we give it. And it identifies patterns and it creates rules by assigning different weights to different patterns.

8:48And that's how it works. And we use Teachable Machine to see that just now using that image project. So instead of 20 to 50 samples, we gave it 100 samples each. But when they were imbalanced, we started to introduce bias. So just remember software 2.0 learns from examples or you can say it learns from data. And every time you train the model, it's going to change the weights depending on the examples that you give it to learn. The idea here is not to have a deep understanding of how this technology works because we really didn't go into all of the details.

9:23We're just talking about this at a high level, which is super important for prompt engineering in the next skill. This is what we're doing. We're setting us up for success in prompt engineering and we can create compliant, private and secure outputs through prompt engineering because we understand that the software that we're using, software 3.0 was created using these rules. It learns from data. And that serves as a foundation for prompt engineering.

9:54It will really start to click when we get into that next skill. And on that note, let's talk about software 3.0 in the next video. See you there.

0:00In software 1.0, we wrote the rules. In software 2.0, data taught the rules. And now in software 3.0, we don't write or train directly. We simply guide the AI with language. And that's why I like to call this plain language programming. You don't need a programming background, but you do need to know some basic prompt engineering. And if you're asking what is prompt engineering, it's plain language programming. What does that mean?

0:30We're guiding the model or the chatbot using plain language, meaning prompts. So prompts are plain language programming. And this is really important when it comes to compliance, privacy and copyright for non-technical teams. We can call this a form of AI governance, meaning you can use prompt engineering for safe, private and copyright friendly outputs for non-technical teams. And the umbrella term here is AI governance, which includes all of that.

1:03And software 3.0 is also called or referred to as generative AI because it generates outputs based on our prompts. So tools like ChatGPT, Gemini, Microsoft Copilot, Grok, Claude from Anthropic, they can write summaries, draft documents or even answer questions. But instead of coding or feeding them structured data, we communicate in natural language by prompting them. Let's see what this looks like in action with a special attention to AI governance.

1:35Let's say that we have some text here. And this is an employee feedback file that I asked the model to generate. And it did so begrudgingly because there's a lot of sensitive information here. But it also is a little funny because it's kind of pretty negative. I didn't ask it to be negative, but this is what we got from the model. And again, that has to do with its training data. So let's put this in quotes like this. And I'm going to say that this is an excerpt. And I'm going to go up here and just say summarize this and include names and performance comments.

2:07Let's see what we get. And by the way, I'm at GPT.com and I'm using my personal account here just to make a point. Normally, when you're working with an organization, you're going to be using the organization account, meaning it's either going to be Google Workspace that uses Gemini or Microsoft that uses Copilot. Very rarely do you use chat GPT and drop documents in there for an organization because compliance in that regard is very difficult to do. Once you have Google Gemini or Microsoft Copilot set up by your organization, they handle the compliance behind the scenes.

2:44The administrator is doing that, which means that you don't have to do that manually. It should be taken care of for you. But when you're using chat GPT like I am here and you're dropping in information like this, which is not real information, so we're OK, it's safe. This is a security issue and a privacy issue and a compliance nightmare. You definitely don't want to do this. And you can click on this button and connect to Google Drive or Microsoft products. But again, you don't want to use these connectors because how can you ensure compliance, privacy and security?

3:19It's really hard to do that. So let's run this and see what we get. OK, so we have a name, we have bonuses, and then we have a project name here, which might be intellectual property. And then we have all of these comments that are associated to this person. And when you combine this project name, this person, these negative comments in this amount, these can be pieced together. And while it might be difficult to do that and the chance of that is not 100 percent, it is still something that you want to avoid.

3:49And you might say, well, isn't this innocent? Well, while it might seem innocent, this request could expose personal or sensitive information because the AI doesn't know what is confidential. It just follows your words. So we can go back up here and we can add something called safety. And I'm just going to say if you find the AI, replace it with placeholders. Let's just try that very simple line. OK, with that one very simple line. Now it's employee A, but we still have project stellar.

4:21So it's not the greatest way to do this. You are protecting the output. However, this is still going into a chatbot. So prompt engineering can help you control what the AI does with language, but it doesn't anonymize data. True compliance starts before prompting. That means that when your organization is collecting this data, they should have removed that data or anonymized it or done something to the data pre-processing so that when it hits the database and it's ready for all of the employees in that organization to consume that data for their work.

4:58You don't want to have to do this in a chatbot. That is the absolute wrong way to do it because it's already gone into the model and you don't want to feed that to the model for many reasons. Even if your chatbot says that it doesn't use the text for training, you still don't want to do that because data leakage can still happen. The worst case scenario is that you are using chat GPT and it's being used for training and you're entering personal data of some individual for a company and maybe even intellectual property.

5:29That's called PII, personally identifying information like employee A's name, their performance for their job and the different bonuses that they've made, financial information. All of that should not be included in the database. And if you do have access to that information, you don't want to put it into the database before it's been redacted or anonymized or filtered and pre-processed in some way. So if you do find this, you can either do it manually before you put it into the model or reach out to some administrator or the data science department.

6:07They're the ones that would handle the pipeline that ingests data and makes sure that it's compliant. And it's not everybody's job, but when you're working with these models, you want to keep an eye out for this. It's very important. And we're going to use prompt engineering to help you identify when this is happening. And you definitely don't want to use it as an output. If you're going to send it in an email, for example, or if it's going to be an output that you're going to include in a newsletter, that's where you really want to make sure that that's not happening.

6:37But if you do identify any PII, IP or other sensitive data, you definitely want to reach out to the appropriate department or team so they can make sure that that data is handled before you use it in any of these models. What I showed you up here, safety. This is part of prompt engineering. And in the next skill, we'll explore concepts like C, R, E, context, role and expectation, as well as something called ask to and safety. This is the foundation for prompt engineering, which will allow you to communicate effectively with software 3.0 models, such as chatbots like chat GPT, Gemini, Copilot, Grok and Claude from Anthropic.

7:22If you're working with these tools, you absolutely are programming in plain language. And that really is the best way to put it, because if you talk to it like a human, because it sounds like a human, that in itself is misleading. And not only will that give you a false sense of security to give it information that you should not be giving it, but it won't give you great outputs either. So prompt engineering does two things. It really handles AI compliance, privacy and copyright, but it also gives you much better output.

7:54So your productivity is going to skyrocket once you use prompt engineering. But furthermore, you're going to create an environment of compliance, safety and privacy at your organization, which is invaluable when you're working with software 3.0. On that note, I will see you in the next part of the scale, which is the challenge. And then we'll get into prompt engineering. Until next time, I hope this has been informative and I'd like to thank you for viewing.