Why RAG Matters More Than the Model

Large language models are impressive, but they have two problems that block most enterprise use cases. They do not know your private data, and they do not know what changed last week. Retrieval-Augmented Generation (RAG) solves both by fetching relevant passages from your own documents at query time and placing them in the prompt. The model then answers from evidence instead of memory.

The idea is simple. The execution is where teams struggle. A demo that answers three questions from a PDF takes an afternoon. A system that answers thousands of questions over ten thousand documents, with citations, access control, and measurable quality, takes a real engineering effort. The quality of a RAG system is almost always decided before the model is called: in ingestion, chunking, embedding, and retrieval.

This article walks through a complete RAG pipeline in Java.…