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Curriculum based on Datawhale / Easy Vibe · CC BY-NC-SA 4.0

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AI Product Builder

Chapter 1: Introduction to Programming
  1. How to Learn This Course
  2. What can you do after the course?
  3. First Programming Experience in the AI Era
  4. 3.3 Try Making Other Mini-Games
  5. 📚 Assignment
Chapter 2: Development Environment and Tools
  1. Introduction to AI Programming Tools
  2. 4. Hands-on: Build a Snake Game Locally with an AI IDE
  3. 6. How to Talk to AI Effectively
  4. 8. 📚 Assignment: Make a More Complex Game with Local AI IDE
  5. Clone from Screenshots: First Imitation Practice
Chapter 3: From Problems to Product
  1. 1. Find Real Problems
  2. 6. Build an Interactive Prototype
  3. 2. Build a Prototype in 10 Minutes: Let AI IDE Implement the Core Gameplay
  4. 4. Make the Prototype Feel Real
  5. 5. 📚 Assignment: Recreate Your Own Douyin E-commerce Workbench
On this page
  • Stage 1: build your first product prototype
  • Why train through projects?
  • The art of asking: a fundamental skill in the AI era
  • Why is this an essential skill?
  • How to give information to AI: screenshot or copy and paste?
  • Prompts that help AI explain well
  • I have tried for a long time and want to give up
  • Some parts of the course design feel unreasonable
  • Reference

What can you do after the course?

Stage 1: build your first product prototype

This stage is for complete beginners and for people who know a little code but are not yet confident. You do not study a large body of theory first. You learn to ask AI to write code and fix errors while building.

After this stage you can:

  • independently complete a web application with an AI coding tool;
  • turn a product idea into a clickable, interactive prototype;
  • add AI features such as image generation and intelligent conversation;
  • investigate and solve errors instead of stopping at the first failure.

In short, you can make something that runs and can be demonstrated to another person.

We begin with a small game, then learn to use an AI coding tool to write code and handle errors. From a simple page, we build an interactive multi-page application, add AI functions, and complete an independent project so an idea has a real chance to reach the world.

Why train through projects?

The challenge of real work

In a real workplace, you are often given a goal but not a complete document, ready-made framework, or detailed requirements.

Your supervisor or customer: We need to build xxx and achieve yyy.

Documentation? Existing framework? Detailed specification? Often none of them exists.

Much real work means solving an unfamiliar problem under uncertainty. Requirements are vague, boundaries change, and nobody provides a standard answer. You must research, experiment, prototype, iterate, and finally deliver a solution that runs, works, and can be released.

Lesson Contents

  1. Stage 1: build your first product prototype
  2. Why train through projects?
  3. The art of asking: a fundamental skill in the AI era
  4. Why is this an essential skill?
  5. How to give information to AI: screenshot or copy and paste?
  6. Prompts that help AI explain well
  7. I have tried for a long time and want to give up
  8. Some parts of the course design feel unreasonable
  9. Reference
7. Connect Real AI Capabilities
  • 7. Image understanding with Qwen3-VL
  • 12. 📚 Chapter assignment
  • 8. Polish and Deliver a Complete Product
  • 6. 📚 Assignment
  • The course gives you a safe simulation of that experience:

    • challenging projects force you to break down problems, design solutions, and find information;
    • scaffolding that is not excessively simplified helps you learn to read, understand, and modify a medium-sized codebase;
    • the full path from idea to release lets you experience a real product journey from zero to one.

    This training can feel difficult in the short term, but it strengthens your ability to handle responsibility, find a way through uncertainty, and turn AI into a real product rather than a demo.

    The art of asking: a fundamental skill in the AI era

    Asking questions is a core skill. With the same code and the same error, how you ask often determines the answer you receive: a vague discussion or an actionable sequence.

    Build the habit: treat asking AI as part of development. When you do not understand something or become stuck, ask at once.

    Why is this an essential skill?

    • Real work rarely has complete documentation: requirements may be unclear, code unfinished, and errors scattered.
    • AI can be a tutor and colleague: good questions turn it into high-quality pair programming.
    • Communication sets the ceiling: the more relevant context and output constraints you provide, the more useful the answer becomes.

    Common mistake: “Why is there an error?” usually produces guesses. Include the context to obtain an executable plan.

    How to give information to AI: screenshot or copy and paste?

    Both work, but for different situations:

    MethodSuitable forKey requirement
    Copy and pasteError stacks, logs, code, configuration, API responsesInclude the complete relevant text, not one keyword
    ScreenshotLayout issues, interaction problems, or a missing button in a toolCapture enough context, mark the important area, and add one sentence

    ⚠️ Important condition

    Not every AI accepts image input. Screenshot-based communication requires a multimodal model that can understand images. Examples include Claude, GPT-4V/GPT-4o, Gemini, and some Chinese models such as Qwen and ERNIE Bot.

    If your AI does not accept images, it cannot read the screenshot. Copy and paste the text instead.

    Prompts that help AI explain well

    If you want to learn the answer rather than only receive it, instructions like these improve the explanation:

    Learning-oriented examples

    • “Explain this concept in five sentences, then ask me a few questions to check my understanding.”
    • “Explain this error in detail. I do not understand why it happened.”

    I have tried for a long time and want to give up

    Perhaps the method, not your persistence, needs to change. Do not struggle alone. Talk with the authors and teaching assistants, describe what you tried, where you became stuck, and how you feel. A small change in direction or one missing concept can often help you continue.

    Some parts of the course design feel unreasonable

    Please contact the authors, open an issue, or give feedback in the class or community. Tell us what is unclear, what feels poor, and where time was wasted. Honest and specific feedback helps later learners avoid the same problem.

    Reference

    • Nanjing University, Computer Systems Fundamentals course experiments

    What to learn next

    Continue from using AI toward building products.

    • First Programming Experience in the AI Era

      Start with a mini game to get hands-on with AI coding, and quickly understand what problems are suitable for AI.

    • Introduction to AI Programming Tools

      Level up from browser playground to a local AI IDE (Trae). Set up a full development environment and learn how to chat with AI productively.

    • Clone from Screenshots: First Imitation Practice

      Practice with a screenshot-based recreation and become comfortable with your AI programming tools.

    • Sources of Creative Inspiration

      Find promising product ideas in real work, daily life, communities, and public feedback.

    • How to Identify a Great Idea

      Move from "I have an idea" to "the right direction" — learn how to discover real user needs and find a product direction people are willing to pay for.

    • Double Diamond Design Model

      Use the Double Diamond to explore problems and converge on a solution worth building.

    • Jobs to Be Done Theory

      Understand the real job users are trying to accomplish behind their stated requests.

    • The Mom Test: User Interview Method

      Validate demand through interviews without mistaking compliments for evidence.

    • Building Interactive Product Prototypes

      From requirement breakdown to page implementation — learn how to generate multi-page apps with an AI IDE and ship your first interactive Demo fast.

    • Integrating AI Capabilities into Prototypes

      Integrate real APIs (text models, image generation). Turn your prototype from "view-only" into "truly usable", and learn model selection & debugging.

    • Complete Project Practice: From Idea to Product

      Fill in the gaps: data persistence, interactions, error handling. Ship a complete, showcase-ready AI product prototype.

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