Already Know Python? Learn to Build Groundbreaking Generative AI
Web Apps
AI is turning everything in tech upside down and inside out. You'll learn how to take the Python skills you already have and use them to build production-ready generative AI apps. And because generative AI is one of the HOTTEST and best-paid areas in tech right now, adding AI to your Python toolkit gets you hired faster. Let’s go!
Already Know Python? Learn to Build Groundbreaking Generative AI Web Apps
AI is turning everything in tech upside down and inside out. You'll learn how to take the Python skills you already have and use them to build production-ready generative AI apps. And because generative AI is one of the HOTTEST and best-paid areas in tech right now, adding AI to your Python toolkit gets you hired faster. Let’s go!
Class 1: Introduction to Developing Generative AI Web Apps
PREREQUISITE: Python
In this class you’ll learn:
- What AI and Generative AI are & all the key terms
- The best practices of prompt engineering
- How the Open AI API works & how to start building a Generative AI app using it
- How to build your very own AI-powered chatbot
Class 1: Intro to Developing Generative AI Web Apps
PREREQUISITE: Python
In this class you’ll learn:
- What AI and Generative AI are & all the key terms
- The best practices of prompt engineering
- How the Open AI API works & how to start building a Generative AI app using it
- How to build your very own AI-powered chatbot
Class 2: Building Complex Generative AI Web Apps With LangChain
PREREQUISITE: Python
In this class you’ll learn:
- How to build more complex Generative AI web applications using the LangChain open source framework
- How to put these skills to use by building a generative AI web application that creates itineraries for trips to national parks in the USA!
Class 2: Building Complex Generative AI Web Apps With LangChain
PREREQUISITE: Python
In this class you’ll learn:
- How to build more complex Generative AI web applications using the LangChain open source framework.
- How to put these skills to use by building a generative AI web application that creates itineraries for trips to national parks in the USA!
Class 3: Enhancing Generative AI Web Apps with Domain Specific Content
PREREQUISITE: Python
In this class you’ll learn:
- How to build contextual, or domain-specific Generative AI web apps using Retrieval Augmented Generation, otherwise known as RAG
- Create your final project, a robust cooking assistant web application.
- You'll use RAG concepts and techniques to enhance the performance of your cooking assistant project.
The real-world projects you’ll build (and be proud to show off)
These aren’t toy demos or throwaway exercises. You’ll build real, production-style generative AI web apps—the kind of projects that show employers you know how to apply AI in the real world.
AI StudyBuddy App
You’ll build your first generative AI web app from scratch: an AI-powered instructional chatbot designed to teach, not just respond. Using the OpenAI API and prompt engineering best practices, you’ll program your chatbot to deliver clear, structured, and educational answers—learning how AI apps actually work behind the scenes, not just how to use them.
AI NatureNook App
In this project, you’ll level up by building a more complex generative AI travel app using LangChain. NatureNook connects to multiple APIs—including the National Park Service, weather data, and OpenAI—to generate accurate, personalized trip itineraries. Based on parameters like group size, preferred activities, and lodging style, your app delivers a full itinerary covering travel, lodging, meals, and activities for U.S. national parks.
AI ChefBoost App
For your final milestone, you’ll build a polished AI-powered cooking assistant using Retrieval Augmented Generation (RAG). ChefBoost is trained on real cookbook data that you extract, transform, and load (ETL) into a vector-based database—allowing the app to deliver smarter, more accurate responses. You’ll also refine the user experience by applying interface updates to the app’s dynamic output, including the ability to toggle responses into clean, readable recipe cards. This is a production-style project that shows you’re ready for real-world AI work.
Class 3: Enhancing Generative AI Web Apps with Domain Specific Content
PREREQUISITE: Python
In this class you’ll learn:
- How to build contextual, or domain-specific Generative AI web apps using Retrieval Augmented Generation, otherwise known as RAG
- Create your final project, a robust cooking assistant web application.
- You'll use RAG concepts and techniques to enhance the performance of your cooking assistant project.
The real-world projects you’ll build (and be proud to show off)
These aren’t toy demos or throwaway exercises. You’ll build real, production-style generative AI web apps—the kind of projects that show employers you know how to apply AI in the real world.
AI StudyBuddy App
You’ll build your first generative AI web app from scratch: an AI-powered instructional chatbot designed to teach, not just respond. Using the OpenAI API and prompt engineering best practices, you’ll program your chatbot to deliver clear, structured, and educational answers—learning how AI apps actually work behind the scenes, not just how to use them.
AI NatureNook App
In this project, you’ll level up by building a more complex generative AI travel app using LangChain. NatureNook connects to multiple APIs—including the National Park Service, weather data, and OpenAI—to generate accurate, personalized trip itineraries. Based on parameters like group size, preferred activities, and lodging style, your app delivers a full itinerary covering travel, lodging, meals, and activities for U.S. national parks.
AI ChefBoost App
For your final milestone, you’ll build a polished AI-powered cooking assistant using Retrieval Augmented Generation (RAG). ChefBoost is trained on real cookbook data that you extract, transform, and load (ETL) into a vector-based database—allowing the app to deliver smarter, more accurate responses. You’ll also refine the user experience by applying interface updates to the app’s dynamic output, including the ability to toggle responses into clean, readable recipe cards. This is a production-style project that shows you’re ready for real-world AI work.
Frequently Asked Questions
1. Do I need previous coding experience in order to take this course?
All you need is Python. We handle the front-end pieces for you with ready-made HTML/CSS/JS files and you’ll focus on the Python that powers generative AI. In the final class, we’ll give you the intro to HTML/JS that you need to understand how everything connects and implement the Gen AI functionality for a complete and usable project.
These classes are designed for students who have completed our Full Stack Career Track. That said, if you're comfortable with all the technologies listed above, you should be good to go! Completion of our Full Stack Track is not a required prerequisite.
If you'd like to first enroll in our Full Stack Career Track, you can do so by signing up for the Break Into Tech Job Training Program.
2. Will I be learning how to write great prompts for ChatGPT?
None of the classes specifically cover prompt best practices for ChatGPT. They do, however, cover extensively best practices for prompt engineering. Prompt engineering is similar to writing prompts for ChatGPT but is a significantly more complex and technical skill—and the exact kind of skill AI Engineers need to know in order to build generative AI web applications.
3. Will I be learning how to make my own AI?
No! Well, at least not your own AI models from scratch. What it means exactly to build your "own AI" is a very squishy and yet to be clearly defined thing. But these classes do not get into the more technical Artificial Intelligence topics of machine learning, building your own AI models, or training an AI model.
Instead, the focus of these classes is how to utilize the AI models available to us (in this instance, the Open AI large language model) to build robust web applications.