From knowledge retrieval to generation: a comprehensive study of RAG's architectures, evolution, and applications

Summary

A survey in Artificial Intelligence Review reviews Retrieval-Augmented Generation (RAG) research through a structured review protocol, organized around system architecture and paradigm evolution from Naive RAG to Agentic RAG, evaluation methods and application evidence, and deployment and security challenges. It names prompt injection, data poisoning, retrieval hijacking, privacy leakage, and citation laundering as emerging risks.

Topics

• Retrieval-augmented generation

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