perspectives. Additionally, we anticipate future direc-
tions for RAG, emphasizing potential enhancements to
tackle current challenges, expansions into multi-modal
settings, and the development of its ecosystem.

The paper unfolds as follows: Section 2 and 3 define RAG
and detail its developmental process. Section 4 through 6 ex-
plore core components—Retrieval, “Generation” and “Aug-
mentation”—highlighting diverse embedded technologies.
Section 7 focuses on RAG’s evaluation system. Section 8
compare RAG with other LLM optimization methods and
suggest potential directions for its evolution. The paper con-
cludes in Section 9.
RAG forLargeLanguageModels-ASurvey