Welcome: What You'll Build in This Course

Most software teams now have the same item on their roadmap: "add AI." Getting a model to answer a prompt takes an afternoon. Getting it to answer well on inputs you've never seen, at a cost that makes sense, inside an interface people trust, takes skills that don't show up in a quickstart guide. That gap between a demo and a feature people rely on is what this course is about.

AI Builder Course teaches you to build AI-powered apps and products: features like summaries, extraction, search over your own documents, assistants that use your app's data, and voice and image input, plus the evals, cost control, safety, and pricing that turn them into something you can ship and sell. This first lesson shows you how the course is organized, the two ways through it, the example app you'll keep returning to, and what you'll have built by the end.

Who this course is for#

You'll get the most from this course if you're one of these people:

• A developer who hasn't shipped AI yet. You're comfortable with JavaScript or TypeScript, you've used ChatGPT or Claude, and now you need to put a model inside a real product. You want the patterns, not a pile of tutorials.
• A technical product person or founder. You may write some code, but your main job is deciding what to build, how it should behave, and whether it will pay for itself. You need to know what's possible, what it costs, and where it breaks.
• A non-developer who wants to build anyway. Module 13 is written for you: assistants, automations, spreadsheet AI, and prompt-to-app tools, with no code required.

You don't need a machine learning background. You won't train a model from scratch or read research papers. You'll treat models as a powerful, slightly unpredictable component and learn to build reliable software around them, which is the skill most AI product work actually requires.

What you'll be able to do by the end#

The course is organized around outcomes, not tools. When you finish, you should be able to:

1. Spot features worth building. Look at a product and tell which problems a language model genuinely solves and which ones it would only make slower, costlier, or less reliable.
2. Build the everyday AI features. Streaming chat, summaries, extraction into typed records, classification and routing, rewriting, inline writing help, and natural-language queries over your data.
3. Ground answers in your own data. Embeddings, semantic and hybrid search, document ingestion, retrieval-augmented generation (RAG, where you fetch relevant documents and hand them to the model), and answers with citations.
4. Pick and run the right model. Choose a model per feature, route between cheap and capable models, and run open-weight models locally or on your own infrastructure when that makes sense.
5. Prove a feature works. Build datasets from real usage, write code-based and model-graded checks, and run them before every change.
6. Ship it responsibly and sell it. Control cost and latency, defend against prompt injection and abuse, handle privacy, deploy and monitor, and price a product whose costs vary with every request.

How the course is organized#

There are fifteen modules. Module 0 (this one) orients you and gets a working feature on your screen. The rest build on each other, but every lesson is written to stand alone, so you can jump to what you need.

Modules 1 through 4 are the foundation. If you're short on time, read those in order, then pick the later modules that match what you're building.

Two tracks: code and no-code#

The course has two paths through the same ideas.

The code track is most of the course. Examples are in TypeScript on Node.js, using the official Anthropic SDK by default and other providers' SDKs where comparison matters. Python shows up where it's the natural choice, such as data work and fine-tuning. Front-end examples use Next.js and React.

The no-code track is Module 13 plus the sixth capstone. It covers what you can build with shareable assistants, automation platforms, spreadsheets, and prompt-to-app builders, and how to recognize when you've outgrown them.

The two tracks share more than you'd expect. The modules on AI UX (Module 8), evals (Module 9), cost (Module 10), and safety (Module 11) apply whether a feature lives in a Next.js app or a no-code automation. A no-code builder who reads Module 8 will design better tools; a developer who skims Module 13 will know when a quick automation beats a sprint of engineering.

Meet Deskmate, the running example#

Many lessons use one fictional product so you can see features fit together instead of floating in isolation. Deskmate is a small help-desk app: customers open tickets, support agents reply, and there's a knowledge base of help articles. It's built with Next.js and Postgres.

A help desk turns out to host almost every AI feature worth learning:

• Summaries of long ticket threads so an agent can catch up in seconds
• Extraction of order numbers, product names, and account details from free-text messages
• Triage: classifying tickets by topic and urgency and routing them to the right team
• Drafted replies grounded in the knowledge base, with citations an agent can check
• Screenshot understanding when a customer attaches an image of an error
• Usage-based billing for the AI features themselves

Each lesson shows all the code it needs, so you never have to reconstruct Deskmate from earlier lessons. Where another domain fits better (an online store, a recipe app, an internal analytics tool), lessons use that instead.

The capstone builds#

Module 14 brings everything together in six complete projects. Each one combines several modules, so it's where scattered skills become a product:

Any of the first five is a reasonable portfolio piece or the seed of a real product. The sixth is proof that a useful AI product doesn't always need a codebase.

How to get the most out of each lesson#

Every lesson follows the same shape: a short intro, sections you can scan from the table of contents, real code and prompts, at least one hands-on exercise, and key takeaways at the end. A few habits will multiply what you get from that:

• Do the exercises with your own data. The examples are realistic, but your inputs will be messier. Running a technique on your own support emails, product catalog, or notes teaches you more than reading about it.
• Keep a playground open. In Your Builder Setup you'll create a scratch project for trying ideas in seconds. Use it constantly.
• Read the outputs. The single most useful habit in AI building is looking at many real outputs, not one or two. You'll hear this often.
• Copy the prompts. Prompt cards have a copy button. Take them, then change them for your product.

One note on scope. This course is about building AI features and products. Autonomous agents, coding agents, and multi-agent systems come up where a product builder needs them, but they're the focus of our sister course; the Agent Builder Course course overview is the place to go deep on those.

ExercisePick the feature you'll build alongside the course

1. Write down one product you work on or want to build, in a sentence.
2. List three things its users do today that involve reading, writing, sorting, or searching text (or images, or audio).
3. For each, write one sentence describing what an AI-assisted version would do for the user.
4. Keep the list. In the next lesson you'll run each idea through a test that tells you whether it's worth building.

Success looks like: three concrete, specific feature ideas, such as "summarize the last 20 messages when an agent opens a ticket," not vague ones like "add AI to support."

Key takeaways

• The course teaches you to build AI features and products that hold up in production, not just demos.
• Fifteen modules go from fundamentals to features, retrieval, models, multimodal, UX, evals, cost, safety, operations, and business, ending with six capstone builds.
• There's a code track (TypeScript first) and a no-code track (Module 13 and Capstone 6); the UX, evals, cost, and safety modules apply to both.
• Deskmate, a fictional help-desk app, is the running example, but every lesson is self-contained.
• Do the exercises on your own data and read lots of real outputs; that habit matters more than any single technique.

Next: What Makes Something an AI Product? — a practical test for telling AI features worth building from ones that only look good in a demo.