ity and flexibility.
It integrates various methods to en-
hance functional modules, such as incorporating a search
module for similarity retrieval and applying a fine-tuning
approach in the retriever [Lin et al., 2023]. Restructured
RAG modules [Yu et al., 2022] and iterative methodologies
like [Shao et al., 2023] have been developed to address spe-
cific issues. The modular RAG paradigm is increasingly be-
coming the norm in the RAG domain, allowing for either a
serialized pipeline or an end-to-end training approach across
multiple modules. The comparison of three RAG paradigms
RAG forLargeLanguageModels-ASurvey