How AI Coding Tools Actually Work (The Model Is the Easy Part)
AI coding tools like Cursor, GitHub Copilot, and Windsurf can generate features, refactor code, and modify multiple files in seconds.
But how do these tools actually work?
Most people assume the process is simple: a developer writes a prompt, the AI generates code, and the editor inserts it into the project.
The reality is much more complex.
Behind modern AI coding tools is an entire orchestration system that scans your codebase, retrieves relevant files, builds prompts for the AI model, applies changes safely, and verifies that everything still works.
In this video we break down how AI coding tools actually work behind the scenes.
You’ll learn:
• How AI models generate code
• How AI IDEs understand your project
• How tools retrieve relevant files from your codebase
• How Retrieval Augmented Generation (RAG) works for code
• How AI systems apply changes safely using diffs and patches
• Why AI coding tools sometimes fail
Understanding this system reveals an important insight:
The AI model itself is often the simplest part of the entire system.
The real complexity lies in the orchestration layer built around it.
If you're interested in AI, coding, and the future of software development, consider subscribing.
⏱ Chapters
00:00 The Magic of AI Coding Tools
00:19 Why AI Doesn't Know Your Files
00:52 The Real Architecture Behind AI Coding Tools
01:39 AI Model is the Simplest
01:48 How AI Model Generates Code
02:54 How IDEs Understand Your Codebase
04:27 Retrieval Augmented Generation (RAG) for Code
05:03 How AI Applies Code Changes
05:28 The Validation Loop
05:57 The Real Secret Behind AI Coding Tools
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