Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (7): 101-117.doi: 10.11896/jsjkx.251000089

• Artificial Intelligence • Previous Articles     Next Articles

Retrieval-Augmented Generation:Survey of Methods and Applications

WANG Xinlin1,2, LI Yan1, MA Chaofan3, LI Shuo1   

  1. 1 Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China
    2 University of Chinese Academy of Sciences,Beijing 100049,China
    3 School of Software,Zhongyuan University of Technology,Zhengzhou 450007,China
  • Received:2025-10-21 Revised:2026-03-10 Online:2026-07-15 Published:2026-07-10
  • About author:WANG Xinlin,born in 2003,postgra-duate.Her main research interest is retrieval-augmented generation.
    LI Shuo,born in 1970,Ph.D,resear-cher,doctoral supervisor.His main research interest is control systems for autonomous underwater vehicles.
  • Supported by:
    National Natural Science Foundation of China(42206196,U23A20645).

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Abstract: Large Language Model(LLM),endowed with human-like capabilities in text generation and natural language understanding,are profoundly reshaping the landscape of artificial intelligence.However,due to the closed nature and lagged updates of their training corpora,LLM often generate responses that deviate from factual accuracy when confronted with dynamically updated information or long-tail knowledge in specialized domains.To address this limitation,Retrieval-Augmented Generation(RAG) extends the knowledge scope of LLM by integrating external knowledge bases,thereby assisting them in producing more accurate and higher-quality answers.Distinct from prior classifications based on processing stages,this paper innovatively categorizes existing RAG approaches around core challenges to be addressed,dividing them into four major categories:chunk optimization,retrieval enhancement,context compression,and knowledge graph integration.This paper systematically reviews representative works and technical mechanisms within each category.Subsequently,it summarizes typical applications of RAG across various specialized domains,and finally discusses future research directions,providing researchers with a clear technical roadmap and practical guidance for method selection.

Key words: Large language model, Retrieval-augmented generation, Chunk optimization, Retrieval enhancement, Context compression, Knowledge graph integration

CLC Number: 

  • TP391
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