The AI Coding Method That Works Every Time

Summary

In this video, the creator shares a simple three-step system for coding with AI that significantly improves consistency. They walk through creating a full-stack web app, using AI to understand and document the application's architecture, and creating a Product Requirements Document (PRD) for a new courses feature. By providing AI with a clear mental model of the codebase and detailed instructions, the process avoids overwhelming the system and ensures better results. The video emphasizes the importance of iterative feedback, MVP development, and tracking progress to create better software with AI-powered tools.

Takeaways

• 😀 Start with a mental model: Before asking AI to code, provide it with a mental model of how your application works. This helps the AI understand the current structure and leads to better results.
• 😀 Document the architecture: Create a detailed document that describes the software architecture, including technical diagrams written in Mermaid, to help the AI grasp the application's full structure.
• 😀 Use AI to analyze the entire repository: Allow the AI to explore your codebase and create a comprehensive document that includes developer and product management perspectives.
• 😀 Keep AI's context updated: Regularly update your documentation with the latest changes in the application to ensure that the AI works with the most current information.
• 😀 Create structured requirements: When asking AI to implement features, start by creating a Product Requirements Document (PRD) that clearly defines requirements and user stories.
• 😀 Use appropriate AI models: Different AI models may excel at different tasks, so selecting the right model for each part of the process (e.g., creative thinking for PRDs) can yield better results.
• 😀 Implement iteratively: Start by having the AI implement the core features with static data before integrating with a live database. This prevents overwhelming the AI and allows for incremental progress.
• 😀 Save progress: Track the progress of both the code and its alignment with the PRD. This helps maintain a clear view of where the project stands and what still needs to be done.
• 😀 Use AI to keep documents up-to-date: When changes are made to the code, use AI to automatically update the PRD with implementation status and next steps, ensuring it reflects the latest progress.
• 😀 Build upon existing work: Utilize the AI's ability to follow established patterns in the codebase and connect to pre-existing components, ensuring consistency throughout the development process.

Q & A

What was the main frustration the speaker faced with AI coding before developing the three-step system?

The speaker was initially frustrated with AI coding because they couldn't get code agents to work properly and found it difficult to integrate AI into the development process.

What does the three-step system help with in AI coding?

The three-step system helps the speaker be more consistent in AI coding, making it easier to develop software with AI assistance, leading to more effective and efficient results.

What is the focus of the web application being developed in the video?

The web application being developed is focused on teaching AI engineering at scale, with features for users, admins, and course creators, including typical functionalities like signing in, registration, and user profiles.

What is the main feature the speaker plans to implement in the course of the video?

The speaker plans to implement a course overview page in the web application, as the current implementation redirects to a 404 error when accessing the courses page.

What mistake does the speaker highlight that many people make when using AI coding agents?

The speaker emphasizes that many people make the mistake of jumping straight into asking the AI agent to create a feature without first providing it with a detailed understanding of how the application works or clear requirements for the feature.

How does the speaker recommend providing the AI agent with an understanding of the application?

The speaker recommends providing the AI agent with a mental model of the application, which involves creating a detailed document that describes the architecture, requirements, and functionality of the app, including technical diagrams made with Mermaid.

What tool or system does the speaker use to generate technical diagrams for the AI agent?

The speaker uses Mermaid, a tool that allows the creation of technical diagrams in markdown format, which can be interpreted by both humans and AI models.

What role does the 'AI Academy analysis markdown' file play in the process?

The 'AI Academy analysis markdown' file serves as a comprehensive document that provides the AI agent with an overview of the application’s architecture, including detailed software, developer, and product management perspectives. This document is used to ensure the AI agent has all the relevant context when making further suggestions or implementations.

What strategy does the speaker use to ensure the AI agent always uses the analysis document?

The speaker configures their AI editor (e.g., GitHub Copilot in Visual Studio Code) to always include the 'AI Academy analysis markdown' file in the agent’s conversation settings, ensuring the agent uses this reference when generating code or providing advice.

How does the speaker ensure the AI's work on the course page aligns with the desired product?

The speaker starts by requesting a product requirements document (PRD) to define the exact needs for the courses overview page. They ask the AI to think like a product manager to create a well-structured PRD before any code is written. This ensures that the AI’s work aligns with the specified user stories and functional requirements.

What is the MVP approach used for the courses overview page implementation?

The speaker asks the AI to implement the courses overview page as a minimum viable product (MVP), which involves creating the front-end components with static data for now. The real database connection and dynamic data will be implemented later, ensuring a simple and functional version first.

How does the speaker keep track of the progress on the course page feature?

The speaker tracks progress by editing the original PRD to include an 'implementation status' section. This section outlines what has been implemented, what is partially done, and what is still pending, which helps keep the AI (and any future developers) informed about the current state of the project.

Why does the speaker choose to include progress tracking in the product requirements document?

By including progress tracking in the PRD, the speaker ensures that any future work or development can easily reference the current status of the feature implementation. This approach helps maintain consistency and accountability throughout the development process.