From Idea to AI Product
In the past, building software had a high barrier: you had to understand programming and algorithms and have years of project experience. Now it's different. As long as you have an idea, AI can help you write the code.
This is a huge change: Programming languages are becoming natural languages.
The emergence of Large Language Models (LLMs) has turned development from a "technical expert's exclusive" into a tool everyone can use. What used to be the hardest part—"how to write code"—is now replaced by the new hardest part: "What do you want to do?"
What is Vibe Coding? Simply put, it's "programming by speaking." Vibe coding means you can rely solely on conversing with AI instead of writing code directly to complete a programming project.
Of course, letting AI write code is just the first step. To make a truly usable product, you will still encounter these questions:
- How to let AI write clean, maintainable code?
- How to piece together scattered code into a runnable application?
- How to make the application truly go live and be used by people?
- How to put AI capabilities like text generation and image recognition into your product?
These questions will find answers in this course.
Whether you are a student, teacher, doctor, worker, or any common person who knows nothing about technology—you don't need to learn programming for years first; in two weeks, you can make a runnable, demonstrable product prototype.
| Your Identity | This Course Can Help You |
|---|---|
| Student | Assignments, competitions, entrepreneurship; do projects yourself without asking for help |
| Professional | Automate repetitive work, improve efficiency, and even develop side hustles |
| Product Manager / Designer | Ideas no longer stay on paper; quickly make Demos to show bosses/clients |
| Entrepreneur / SME Owner | Validate ideas at low cost; make an MVP without spending tens of thousands on outsourcing |
| Teacher / Educator | Make teaching tools, courseware, and automated questions to improve teaching efficiency |
| Doctor / Lawyer / Professional | Automate professional processes and build your own efficiency tools |
| Anyone | Use AI to solve specific problems in life/work, making the impossible possible |
In the AI era, execution and ideas are always more important than technology.
Growth Path: From "Using AI" to "Making AI Products"
Getting Started
Experience AI Programming
Stage One
Product Manager / Operations
Stage Two
Junior-Mid Developer / Indie Dev
Stage Three
Senior Developer / Architect
Through this complete learning path, you will gain:
- Vibe Coding Development Ability: Effortlessly use vibe coding thinking and AI coding tools to increase development efficiency several times. No longer need to memorize syntax, but learn how to guide AI to generate high-quality code.
- Full-stack Development Skills: From UI design to front-end implementation, from database design to API development, and from local development to cloud deployment, master the full technology stack of modern Web applications.
- AI Capability Integration: Learn to call various multimodal AI APIs and seamlessly integrate AI capabilities like text, images, and voice into your applications, building intelligent products through technologies like RAG.
- Product Thinking and Operations Ability: From user research to demand deconstruction, from MVP design to product iteration, and from payment integration to user management, form a complete product development and operation closed loop.
What Can You Do After Learning?
Stage One: Build Your First Product Prototype
This stage is suitable for students with zero programming foundation or those who only know a little but are not confident. You don't need to learn a lot of theoretical knowledge first, but follow the steps directly and learn how to use AI tools to write code in the process.
After learning, you can:
- Independently complete a web application using AI programming tools
- Turn product ideas into clickable, interactive prototypes
- Add AI functions to the prototype (e.g., text-to-image, intelligent dialogue)
- Know how to troubleshoot and solve problems when encountering errors
Simply put, you can make something "runnable and demonstrable to others."
We can first experience AI programming through mini-games, then learn how to use AI programming tools to help you write code and fix errors. Then start from simple pages and gradually make interactive multi-page applications, adding AI functions like text-to-image and intelligent dialogue. Finally, independently complete a full project so that your creativity can truly have the possibility of landing.
Why Use Project-Based Training?
Real-world Challenges
The reason is simple: based on the state of most students now, directly entering the workplace might make it difficult to move an inch under the "social beatings" of real projects and bosses/clients. More common scenarios in the real world are:
Your mentor / boss: We want to do xxx, the goal is to achieve yyy effect.
Documentation? Ready-made frameworks? Detailed requirement specifications? Often they don't exist.
Many tasks in real work are essentially solving problems never seen before in a highly uncertain environment: requirements are vague, boundaries are changing, no one tells you the standard answer, and you need to look up information, do experiments, build prototypes, iterate continuously, and finally give a "runnable, usable, and launchable" solution.
What this course wants to do is give you a "simulated social beating" in advance in a relatively safe environment:
- Force you to practice deconstructing problems, designing solutions, and finding information yourself through seemingly difficult project tasks.
- Allow you to learn to read, understand, and transform a medium-to-large codebase through scaffolds and code that are not so "idiot-proof."
- Let you experience the complete process of a real product from 0 to 1 through the complete closed loop from idea to launch.
In the short term, this kind of training is indeed torturous; but in the long term, it will greatly improve your competitiveness in job searching and career development: you will be more able to handle things, more able to find breakthroughs in uncertain environments, and more capable of turning AI into real landing products instead of staying in the "playing with demos" stage.
The Art of Questioning: An Essential Skill in the AI Era
In the AI era, questioning is also a "basic skill." For the same code and the same error, how you ask almost determines what kind of answer AI can give: whether it's talking broadly or giving implementable modifications step by step.
Develop Good Habits: Treat "asking AI" as part of the daily development flow: ask immediately when you don't understand or get stuck.
Why is this an Essential Skill?
- Real life rarely has complete documentation: Most of the time you face unclear requirements, half-finished code, and scattered error messages.
- AI is your tutor + colleague by your side: Those who can ask questions can turn it into "high-quality pair programming."
- Ability upper limit is determined by communication: The more you can provide key information and the more you can constrain the output format, the more usable the answer will be.
Common Misconception: Asking just "Why error?" usually only gets a bunch of guesses. Only by filling in the context will you get an executable solution.
How to "Feed" Information to AI: Screenshots vs Copy-Paste
Both methods are fine, but for different purposes:
| Method | Applicable Scenarios | Key Requirements |
|---|---|---|
| Copy-Paste | Error stacks, logs, code, configuration, API returns | Be as complete as possible; don't just take one line of keywords |
| Screenshot | UI layout issues, interaction anomalies, can't find buttons in tools | Screenshot full screen + highlight key areas, preferably with a line of text description |
⚠️ Important Prerequisite
Not all AI support image input. Communication via screenshots requires AI to have multimodal capabilities (i.e., the ability to understand and analyze images). Current AIs that support image input include: Claude (Anthropic), GPT-4V/GPT-4o (OpenAI), Gemini (Google), and some Chinese models like Tongyi Qianwen, Wenxin Yiyan, etc.
If the AI you are using does not support image input, screenshots will not be recognized. In this case, please switch to copy-pasting text for communication.
Prompt Tips to Make AI "Explain Well"
If you don't just want the answer, but want to "learn" the answer. Using instructions like the following can significantly improve the quality of explanation:
Learning Question Examples
- "Please explain this concept clearly in 5 sentences first, and then ask me a few questions to verify if I understood it correctly."
- "Please explain this error message in detail; I don't understand why the error occurred."
I've been persistent for a long time but still can't handle it, I want to give up
Maybe your method of persistence is wrong. Don't hold on alone in the dark; you can come and talk to the authors and teaching assistants: frankly state the methods you have tried, the specific stuck points you encountered, and your current state of mind. Many times, just a slight adjustment in direction or adding a key knowledge point can keep you moving forward.
I feel some designs of the tutorial are unreasonable
You are welcome to contact the author at any time, submit an issue, or give feedback directly in the group/class. We very much hope to work with you to polish this set of tutorials to be better and better: wherever it's unclear, wherever the experience is broad, or wherever it makes you waste effort, you can point it out frankly. The more real and specific the feedback, the more it can help newcomers avoid pitfalls.