When you paste a GitHub URL into RepoExplainer, what actually happens? How does an AI model go from a list of files to a coherent explanation of an entire codebase? Let's break it down.
Large language models like Claude process text as tokens — roughly 4 characters per token. A typical repository has millions of characters, far more than any model can process at once.
RepoExplainer doesn't blindly send every file. It prioritizes: README, package.json/requirements, main entry points, configuration files, and key source files. This gives the model the most signal per token.
Modern LLMs are trained on billions of lines of code from GitHub. They recognize patterns — MVC architecture, REST APIs, React components — without being explicitly told what they're looking at.
The model synthesizes everything into a human-readable explanation: what the project does, how it's structured, the tech stack, and how to run it. It's like having a senior developer review the repo for you.
AI analysis isn't perfect. It can miss subtle architectural decisions, misinterpret domain-specific code, or overlook important context in comments. Always use AI explanations as a starting point, not the final word.