Lastly, the engineering practice of RAG is a significant
area of interest. The ease of implementation and align-
ment with corporate engineering needs have contributed to
RAG’s rise. However, in engineering practice, questions
like how to improve retrieval efficiency and document re-
call rate in large-scale knowledge base scenarios, and how
to ensure enterprise data security, such as preventing LLMs
from being induced to disclose the source, metadata, or
other information of documents, are crucial issues that need
resolution[Alon et al., 2022].
Horizontal expansion of RAG
Research on RAG has rapidly expanded in the horizontal
field. Starting from the initial text question answering do-
main, RAG’s ideas have gradually been applied to more
modal data, such as images, code, structured knowledge, au-
dio and video, and so on. There are already many works in
this regard.
In the
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey