Beyond RAG: Architecting Local Long-Context Pipelines with Gemma 4's 31B Dense Model

Most AI document processing relies heavily on Retrieval-Augmented Generation (RAG). We chunk data into tiny pieces, vectorize it, and stitch the summaries together. RAG is excellent for finding a needle in a haystack, but it is fundamentally flawed when you need the model to understand the entire haystack at once. With the release of Gemma 4, specifically the native 128K context window , we finally have the tools to move away from aggressive chunking. In this post, I’ll break down why long-conte