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This review aims to synthesize the evolution, architectures, applications, and challenges of retrieval-augmented generation in large language models.

Retrieval‐Augmented Generation for Large Language Models: Evolution, Architectures, Applications, and Challenges (2020–2025)

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This review aims to synthesize the evolution, architectures, applications, and challenges of retrieval-augmented generation in large language models.
Conducted a PRISMA-guided synthesis of 91 studies published from January 2020 to December 2025.
Analyzed various retrieval techniques, architectures, and their impact on model performance including precision and recall.
Reviewed domain-specific requirements for RAG implementations in healthcare, legal, enterprise, and educational systems.
Identified critical influences of retrieval techniques on LLM performance metrics such as recall and precision.
Highlighted the necessity for evaluation methods that account for the multifaceted nature of retrieval quality and model trustworthiness.
Outlined ongoing research challenges in creating secure and efficient domain-aware RAG systems.

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Advanced review explores retrieval-augmented generation's evolution and evaluates applications in various domains, suggesting improvements.

Faridi et al. (2026) studied this question.

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Advanced review explores retrieval-augmented generation's evolution and evaluates applications in various domains, suggesting improvements.