In this course, we help you build a foundation of knowledge about artificial intelligence (AI), to add features to your websites and web applications. You've likely already used AI in some capacity, such as prompting in Gemini or ChatGPT's interface, reading generated summaries in Google Search, or vibe coding with tools like Antigravity. Here, you learn decision frameworks to design and integrate AI into your applications.
If you're familiar with the web.dev Learn courses, such as HTML, Accessibility, and Privacy, this may look a little different. We know that engineering roles are changing and will continue to change. While reading and writing code is still a critical part of a web developer's job, your most important role with AI is planning your system.
It's impossible to write this course and remain relevant if we offer explicit focus on any one tool or model. Besides, that's what documentation is for. Instead, this course focuses on more essential skills.
We aim to help you answer the following questions:
- What feature are you building, and why are you building it?
- Is AI the right tool to make this feature possible or the right tool to help you produce the application?
- What determines if your feature is successful?
Senior engineers know you should always plan a system before you build, to ensure you meet a set of predetermined expectations. This can include system safety, accessibility, simplicity (when possible), and scalability. Now, everyone has to think about application architecture, before you start building.
As AI evolves, you're more likely to become a system architect. Instead of jumping right in and building, you need to construct how something is built, document your requirements, and determine where and how AI belongs. AI may be a feature in your application, or it may write code and support your development process. In the end, you determine what works, how to mitigate problems, and how to meet the expectations of your end users.
Whether prompted by company mandates or enthusiasm, AI for AI's sake isn't useful. The best features come from a user need and are measured by the value they deliver.
You'll how to think about building your web applications with an AI system that is responsible, usable, and valuable, so that your application delivers on the technology's promise.
Introducing AI for Web developers
Introducing basic principles and mental models to help you think about your own AI use cases and solutions.
Explore AI use cases
You shouldn't build AI features because they're novel or impressive, but because they genuinely make life easier, faster, or more enjoyable for users. This module describes a structured, iterative method for ideating, specifying, and prototyping AI use cases in your product.
Predictive AI
Learn about predictive AI, how it works, and how it can be used in web development.
Generative AI
Understand if you need a generative AI model for your web application.
Build responsibly with AI
In this module, we cover privacy, fairness, and trust. Your design decisions directly shape the responsibility and safety of your AI system.
Pick your platform
Your choice impacts the speed, cost, scalability, and trustworthiness of your AI system.
Choose a client-side library
Understand your options for client-side AI, what trade-offs to expect, and how to handle application-specific constraints.
Prompt engineering
Learn how prompt components are distributed in a system, basic techniques, and scenarios in which to apply them.
Evaluation-driven development
Use this development framework to balance brevity with effectiveness in your AI applications, based on test-driven development.
UX patterns for web AI
Discover best practices for designing UX for background, constrained, and open-ended AI patterns.
Glossary
Many of the key terms and concepts from this course.
So, are you ready to learn AI? Let's get started.