Despite the strides made in RAG technology, research op-
portunities abound in improving its robustness and its abil-
ity to manage extended contexts. RAG’s application scope is
also widening into multimodal domains, adapting its princi-

ples to interpret and process diverse data forms such as im-
ages, videos, and code. This expansion underscores RAG’s
significant practical implications for AI deployment, attract-
ing interest from both academic and industrial sectors. The
growing ecosystem of RAG is underscored by an increase in
RAG-centric AI applications and the ongoing development
of supportive tools. However, as RAG’s application land-
scape expands, there is an imperative need to refine evaluation
methodologies to keep pace with its evolution. Ensuring that
performance assessments remain accurate and representative
is crucial for capturing the full extent of RAG’s contributions
to the AI research and development community.
RAG forLargeLanguageModels-ASurvey