Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

ArXi:2411.19463v3 Announce Type: replace-cross Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges when making RAG deployment decisions. While existing research prioritizes algorithmic innovations, a systematic gap persists in understanding fundamental engineering trade-offs that determine RAG success.