vide a comprehensive overview and analysis of existing RAG
technologies and offer conclusions and prospects for future
development methods. This survey intends to furnish readers
and practitioners with a thorough and systematic comprehen-
sion of large models and RAG, elucidate the progression and
key technologies of retrieval augmentation, clarify the merits
and limitations of various technologies along with their suit-
able contexts, and forecast potential future developments.
RAG forLargeLanguageModels-ASurvey