Abstract: This paper conducts a thorough survey of Retrieval-Augmented Generation (RAG), emphasizing its ability to merge retrieval methods with deep learning to overcome limitations of large language models. It categorizes the RAG process and discusses key studies that have shaped its development.

Highlights:

• Describes the evolution of RAG and its functions across text-retrieval generations.
• Introduces evaluation methods for analyzing RAG’s effectiveness including its challenges and future research directions.
• Offers a comprehensible framework aimed at consolidating research and evolution of RAG technologies.

Importance: The study provides a structured insight into RAG, gearing it toward practical improvements and wider adaptability in LLM applications. It serves as a critical resource for those looking to understand or enhance the dynamic capabilities of LLMs.