Building a Token‑Efficient RAG System for Large Books
A Practical Tutorial
Retrieval‑Augmented Generation (RAG) is one of the most powerful ways to build AI‑driven applications that answer questions using your own data. But if your reference material is large, for example, a full book, and you’re using a plan with limited context (such as Claude Pro), you’ll quickly run into token limits, high costs, and slow responses.
This tutorial walks you through a production‑ready, token‑efficient RAG architecture that solves these problems. You’ll learn how to preprocess Markdown files, store them efficiently, retrieve only what matters, compress context, and scale your system to many users without blowing your token budget.
