CAG: Chunked Augmented Generation for Google Chrome's Built-in Gemini Nano

Integrating Gemini Nano into Google Chrome marks a revolutionary shift in browser capabilities, transforming it from a simple content delivery platform into an intelligent processing environment. This native AI integration addresses several longstanding challenges: it eliminates external API dependencies, enhances privacy through local processing, and democratizes AI access by making these capabilities available to all Chrome users without additional software or API requirements. However, browser-based AI models face a significant constraint in their limited context window size, which restricts their ability to process larger inputs like extensive documents or codebases. This limitation emerges from the necessary balance between model capability and browser performance constraints, potentially hindering real-world applications requiring substantial data processing. To address this challenge, we introduce Chunked Augmented Generation (CAG), an architectural framework specifically designed for Chrome's Gemini Nano implementation.