Large Language Models (LLMs) are evolving into a new kind of operating system, with retrieval augmented generation (RAG) playing a crucial role. This article explores the challenges and solutions in RAG methods, including query transformations, routing, query construction, indexing, and post-processing. It also discusses the potential of open-source models and the need for benchmarks in assessing these approaches.
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