What this book covers

Chapter 1, It’s a New World, One with AI Assistants, and You’re Invited, looks at how we started using large language models and how it constitutes a paradigm shift for many, not just IT workers.

Chapter 2, Prompt Strategy, explains the strategy used throughout the book in terms of breaking down a problem and some guiding principles on how to effectively prompt your chosen AI tool.

Chapter 3, Tools of the Trade: Introducing Our AI Assistants, is where we explain how to work with our two chosen AI assistants, GitHub Copilot and ChatGPT, covering everything from installation to how to get started using them.

Chapter 4, Build the Appearance of Our App with HTML and Copilot, focuses on building the frontend for our e-commerce app (a narrative you will see featured throughout the book).

Chapter 5, Style the App with CSS and Copilot, is where we keep working on our e-commerce app but now focus specifically on CSS and ensuring the appearance is appealing.

Chapter 6, Add Behaviour with JavaScript, is where we add behavior to our e-commerce app using JavaScript.

Chapter 7, Support Multiple Viewports Using Responsive Web Layouts, is where we address the fact that an app needs to work for different device types, whether it’s a smaller mobile screen, a tablet, or a desktop screen. Therefore, this chapter focuses on responsive design.

Chapter 8, Build a Backend with Web APIs, looks at how, for the app to actually work, it needs to have a backend, consisting of code that’s able to read and write data and persist it. This chapter therefore focuses on building a Web API for our e-commerce app.

Chapter 9, Augment Web apps with AI Services, covers training a machine learning model and how to expose it via a Web API for consumption by anyone with a browser or other type of client capable of using the HTTP protocol.

Chapter 10, Maintaining Existing Codebases, covers how most developers work on existing code and maintain existing codebases rather than creating new projects. Therefore, this chapter focuses on various aspects of maintaining code, like dealing with bugs, performance, working with tests, and more.

Chapter 11, Data Exploration with ChatGPT, is where we work with a review dataset and learn to identify insights into distribution, trends, correlation, and more.

Chapter 12, Building a Classification Model with ChatGPT, looks at the same review dataset as in Chapter 11, this time performing classification and sentiment analysis.

Chapter 13, Building a Regression Model for Customer Spend with ChatGPT, attempts to predict the yearly amount spent by customers and uses regression to create a model capable of making this prediction.

Chapter 14, Building an MLP Model for Fashion-MNIST with ChatGPT, looks at building an MLP model based on a fashion dataset, still sticking to our general theme of e-commerce.

Chapter 15, Building a CNN Model for CIFAR-10 with ChatGPT, focuses on building a CNN model.

Chapter 16, Unsupervised Learning: Clustering and PCA, focuses on clustering and PCA.

Chapter 17, Machine Learning with Copilot, covers conducting machine learning using GitHub Copilot to contrast it with ChatGPT.

Chapter 18, Regression with Copilot Chat, is where we develop a regression model. Also, this chapter uses GitHub Copilot.

Chapter 19, Regression with Copilot Suggestions, like the preceding chapter, focuses on regression using GitHub Copilot. The difference between this and the preceding chapter is that here we use the suggestions from writing prompts as comments in a text file, rather than writing our prompt in a chat-like interface.

Chapter 20, Increasing Efficiency with GitHub Copilot, focuses on getting the most out of GitHub Copilot. This chapter is a must read if you want to master GitHub Copilot.

Chapter 21, Agents in Software Development, takes a look at what’s coming next within AI, namely, agents. Agents are able to assist you to a much higher degree by acting autonomously based on a high-level goal. This is definitely worth a read if you’re curious about future trends.

Chapter 22, Conclusion, wraps up the book by drawing some conclusions as to the greater lessons learned about working with AI assistants.