Retrieval-Augmented Generation (RAG) is central to many LLM applications, but making RAG systems work reliably and efficiently in production presents specific challenges. This chapter addresses the data integration and retrieval pipeline, focusing on scalability and performance.

You will learn techniques for processing diverse document types effectively, selecting and optimizing vector stores for demanding workloads, and implementing sophisticated indexing and search strategies, including hybrid search and result re-ranking. We will also cover methods for keeping your indexed data current. The chapter concludes with building a complete, optimized RAG pipeline as a practical exercise.