  Retrieval-Augmented Generation (RAG) merges retrieval methods with deep
learning advancements to address the static limitations of large language
models (LLMs) by enabling the dynamic integration of up-to-date external
information. This methodology, focusing primarily on the text domain, provides
a cost-effective solution to the generation of plausible but incorrect
responses by LLMs, thereby enhancing the accuracy and reliability of their
outputs through the use of real-world data. As RAG grows in complexity and
incorporates multiple concepts that can influence its performance, this paper
organizes the RAG paradigm into four categories: pre-retrieval, retrieval,
post-retrieval, and generation, offering a detailed perspective from the
retrieval viewpoint. It outlines RAG's evolution and discusses the field's
progression through the analysis of significant studies. Additionally, the
paper introduces evaluation methods for RAG, addressing the challenges faced
and proposing future research directions. By offering an organized framework
and categorization, the study aims to consolidate existing research on RAG,
clarify its technological underpinnings, and highlight its potential to broaden
the adaptability and applications of LLMs.
