  Large Language Models (LLMs) showcase impressive capabilities but encounter
challenges like hallucination, outdated knowledge, and non-transparent,
untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has
emerged as a promising solution by incorporating knowledge from external
databases. This enhances the accuracy and credibility of the generation,
particularly for knowledge-intensive tasks, and allows for continuous knowledge
updates and integration of domain-specific information. RAG synergistically
merges LLMs' intrinsic knowledge with the vast, dynamic repositories of
external databases. This comprehensive review paper offers a detailed
examination of the progression of RAG paradigms, encompassing the Naive RAG,
the Advanced RAG, and the Modular RAG. It meticulously scrutinizes the
tripartite foundation of RAG frameworks, which includes the retrieval, the
generation and the augmentation techniques. The paper highlights the
state-of-the-art technologies embedded in each of these critical components,
providing a profound understanding of the advancements in RAG systems.
Furthermore, this paper introduces up-to-date evaluation framework and
benchmark. At the end, this article delineates the challenges currently faced
and points out prospective avenues for research and development.
