Welcome to this course, where you learn how to prototype generative AI applications fast using Streamlit. This course is created in a collaboration between DeepLearning.ai and Snowflake, which manages Streamlit. Today, people that know how to build with GenAI are moving faster than was ever possible before. And the ability of AI to write code for you, to problem solve, to plan out complex software applications, is dramatically speeding up the rate at which all of us can build software. It is now possible, and in fact is often preferable, to explore ideas using actual working software prototypes, rather than just using a piece of paper or documentation that gives some specification or some hand-drawn wireframe. When you build something quickly, you can take that software prototype and get feedback. And this rich feedback lets you decide what to do for the next iteration, and you can make progress and get real feedback to drive that progress much faster than was possible before. So in this course, you learn a mental framework for thinking about how to build prototypes fast and how to iterate quickly. I'm thrilled to be here with the instructor, Dr. Shanin Nanta Selavan, who also created the YouTube channel, The Data Professor, which has helped thousands of developers bring their AI ideas to life. Welcome, Shanin. Yeah, thanks Andrew for the kind introduction. I know you've been talking a lot about how GenEye has completely transformed your own development process. What's the biggest change for you personally? Working in AI and software, I feel like it's been an exciting field for a while, but I felt like I was on a roller coaster, just moving slowly. And I wasn't bored. It is a roller coaster, happy roller coaster. But the recent changes in the genetic coding, it feels like, boy, this roller coaster is moving much faster. And that speed and velocity, I find very exciting. So these days, I actually end up writing code almost every weekend. I have more time on Saturdays and Sundays than weekdays to write code. But I find that if I have an idea, I can go to a coffee shop on a Saturday afternoon, sit down and get something up and running in one to four hours. And it turns out a lot of my ideas are bad ideas. I implement it, look at it and go, gee, that doesn't work. And then they will never see the light of day. And that's fine, because I wasted only a small amount of time. Sometimes I build something, I go, you know what, I actually like this. I'll take it to my team to see if there's interest in taking it to scale. But that speed and that dramatic reduction in cost of trying something out means that I, and I see many smart teams around the world, now willing to just take a lot more shots. Try it. If it doesn't work, it's okay. We just take a lot more shots to discover things that are really working and worth taking to scale. I see developers moving from let me plan every detail to let me test this one idea. When prototyping is fast, the real bottleneck becomes waiting for feedback. If you can get feedback on day three instead of week three, you have more time to make meaningful improvements. But Andrew, when development moves so quickly, how do you make sure you're learning something useful for each iteration? One practice I often use when sitting down to prototype, I'll be driven that sometimes in the coffee shop, I only have two hours. And what I'll often do is keep on cutting scope until the project I want to implement is doable in two hours. Sometimes you can cut scope to be small enough to build one component of an Envision much larger application. Then take that component and show the users and see if they like it. So for example, if you have, I don't know, say Envision for how to help users process email better. Maybe you can cut scope to build an MVP to take just a handful of emails, maybe copy paste it from your email and then have something to just show a result. And you can take that to user testing or even test it on yourself. And if your own gut is not bad, sometimes there's some dangers to testing on yourself. But sometimes if you hone your guts to predict what the user will want, I find that that's good enough to make really fast decisions so that the pace of decision-making on the product can match the pace of execution that is now possible. With your own snowflake and also running a YouTube channel, what have you seen in terms of the developer mindset shifting to this fast prototyping methodology? Yeah, it's amazing to watch people have that light bulb moment when they realize they can deploy and get feedback on an idea in days instead of months. The developers who pick this up fast are usually the ones who've made this mistake before. They spent months building something, launch it, and only then discover users wanted something totally different. When I talk to my co-workers at Snowflake, one of the immediate concerns would be would rapid prototyping compromise the quality of apps that are developed? So what are your thoughts about that? You know, I think it's interesting that often it is the rapid iterations that helps you get to quality. Because if we start off not really knowing exactly what users want, it's that process of building something, get feedback, build something, get feedback, that allows us a much deeper user insight so they get to something that's actually really good for users and takes into account their needs much more deeply. Having said this, there's one asterisk to this, which is when you take a product to scale, I think good software engineering fundamentals does matter. If you're building a scale, production-grade, secure application, understanding that software fundamentals of what database you use, how to secure the software, how to deploy at the expense of the cloud, that deep set of knowledge is still really valuable. So not everything in life is just, you know, buy coding and let's just do stuff quickly and throw stuff out there. But in the initial phases of the project, where what features to build, is it even worth building when all that's unclear, that rapid prototyping to then decide what to make that deeper technical investment to scale in a robust and reliable and secure way, that seems to be a really important piece of the puzzle. You know, you've done a lot. We'd love to share what are the frameworks you found most useful for driving this type of rapid progress. I found that the best tools for rapid iteration are the ones that remove system integration entirely. You can write code fast with Gen AI, but connecting all the front-end and back-end is where you lose speed. That's why we built this course using Snowflake and Streamlet. They solve this perfectly. Snowflake manages your data, Streamlet creates instant UIs. So you can iterate on your core idea instead of spending a lot of time debugging. As someone with a stronger back-end and front-end background, you know, I'm just not very good at front-end. I found that being able to use Gen AI to write Streamlet code or write JavaScript code just take care of the front-end for me. I ended up writing a lot more front-end code these days, or rather giving AI to write a lot more front-end code for me than I used to. So that's been great. Why don't you walk us through what everyone will see in this course? Sure. You'll be building a sentiment analysis dashboard for a fictional sports gear company called Avalanche. It's based on a pretty common real-world scenario. Your data is messy, the project keeps evolving, and everyone needs insights that they can understand and use. As you build, you'll use Gen AI tools to help write, troubleshoot, and iterate on your code like a true code developer. Then you'll connect your dashboard app to customer-reviewed data stored in Snowflake and set up the mechanics needed to query it efficiently. You'll improve your dashboard by adding a chatbot and using Prompt Engineering and Retrieval Augmented Generation, or RAG, to ground its answers in actual data. Finally, you'll deploy your app and practice gathering feedback because development doesn't end at launch. It starts there. In the end, you'll have created a functional app that demonstrates how to turn data into insights you can share with your team. And you'll have mastered an effective workflow for turning your ideas into working prototypes. And that's really the goal of the course, to help you hone your skill of fast, thoughtful iteration so that you can take exciting ideas and bring them to life with tools and make this idea accessible to prospective users to get feedback and keep building on. So if you have some ideas, and it's also okay if not, there are some ideas you've been thinking maybe you should build that someday, but if you're not sure where to get started, I hope that the step-by-step approach of this course will help point you in some directions to get started from maybe a vague or tentative concept to getting a working prototype. Hope you find the skills you learn in this course useful for your work as well as for your fun personal projects. And with that, I hope you go on to the next video where Shannon will share how GenEI has fundamentally changed prototyping and what this means for how you approach building applications. So let's go on to the next video.
Module 1: Introduction to Prototyping Generative AI Applications
Certificate