How to Build and Sell $1M Apps with AI (What No One Tells You)

The video presents three key strategies for building AI-powered applications more effectively: modularization, patterns, and documentation injection. Modularization involves breaking down an application into smaller, manageable parts, making it easier for AI to understand and update code. Patterns leverage existing well-designed components as a reference for new tool development, ensuring consistency in layout and style. Documentation injection means providing AI with specific API or tool documentation within the prompt, enabling it to understand and correctly implement external functionalities.

These techniques aim to improve the AI's ability to generate functional, scalable applications by providing clearer instructions and context, thereby reducing errors and development time. The speaker emphasizes that while the initial prompt might seem simple, these three tricks are crucial for creating robust applications that can be scaled and maintained.

The ultimate goal is to move beyond basic AI-generated tools to more professional and scalable applications. By applying modularization, patterns, and documentation injection, developers can significantly enhance the quality and efficiency of their AI-assisted development process.

• Modularization involves breaking down applications into smaller, independent parts for easier AI understanding and updates.
• Patterns utilize existing, well-designed components as a reference for new tool development, ensuring consistent style and layout.
• Documentation injection means providing specific API or tool documentation within prompts for AI to accurately use external functionalities.
• These three tricks aim to improve AI's code generation, reduce errors, and enable the creation of scalable applications.
• The speaker has 55 tools built, targeting 100 by the end of 2025.

AI Tool Creation and Development Approach [00:00]

• A simple two-line prompt can be used to create a new, fully functional tool, such as an image pixelator.
• This method is used to build all the tools on 'tool box', with 55 tools currently available and a target of 100 by the end of 2025.
• The secret to this efficiency lies in three specific tricks not commonly discussed.

The secret is in three tricks that I didn't find anyone talking about.

The Core Idea: Better Input, Better Output [01:45]

• Prompt engineering, the art of talking to AI for better output, is still relevant.
• A newer concept, context engineering, emphasizes that the better the input provided to AI, the better the results.
• This is analogous to the machine learning concept of "garbage in, garbage out."

The better you feed AI, the better you get results.

The Traditional vs. Improved AI Application Development [02:27]

• 99% of people build AI applications by describing the application's purpose and requesting a professional UI, then expecting a high-quality outcome immediately.
• While this method can sometimes yield impressive results, problems arise when adding features, complexity, security, or scaling the application.
• This is where the majority of developers using the traditional approach tend to fail.

99% of people, and I think you are one of them, develop applications with AI this way.

Trick 1: Modularization [03:37]

• Modularization involves splitting an application into distinct modules or Lego blocks.
• Instead of the AI scanning the entire codebase to make an update or add a feature, it can focus on a specific module.
• This makes it easier for the AI to understand the code, preventing hallucinations, breaking things, or deleting unintended parts, leading to a more robust application.
• When using an IDE with an AI extension, developers should instruct the AI to develop the application in a modular way from the beginning.
• The AI should be prompted to plan the application correctly and build it by splitting it into multiple tiny pieces.
• The speaker's own code consists of many small files, each with a specific role, allowing for precise AI updates.

If you want to update something, add a feature, your AI model will need to scan all these Lego bricks, all your code to update a feature.

Trick 2: Patterns [07:08]

• This trick involves using "related keywords as a reference for layout and style."
• Developers should spend time perfecting the UI, style, layout, security, and optimization of one core tool.
• Then, this established tool can be referenced by AI for subsequent tool development, ensuring consistency in design and functionality.
• The speaker demonstrates this by showing multiple tools (PNG to WEBP converter, WEBP to JPEG converter) that share the same style and layout, having been developed following the same pattern.

Use the related keywords as a reference for layout and style.

Trick 3: Docs Injection [09:27]

• This involves injecting documentation directly into the prompt when using external APIs or tools.
• Instead of just telling the AI to use an API, provide it with example requests and responses from the API's documentation.
• This gives the AI clear context on how to interact with the external feature, preventing misunderstandings and improving accuracy.
• The speaker also mentions a library called 'simple lm' and an MCP server that can connect AI to documentation, though the simpler method of copying documentation into the prompt is accessible to everyone.

You go here and inject the documentation like say docs and then copy Copy this request and response and paste inside your prompt giving it as a context.

Next Steps and Further Learning [12:21]

• With these three tricks, developers can build applications today using AI.
• For building real, scalable, million-dollar apps, nine additional rules for AI coders are explained in detail in another video.

But if you want to make it into production and build a real scalable $1 million app, we still have nine rules you have to follow as an AI coder.