A Comprehensive Systematic Review of Retrieval-Augmented Generation (RAG): Developments, Limitations, and Future Pathways

Abstract Widespread deployment of large language models (LLMs) across knowledge-intensive industries has brought their core architectural weakness into sharp focus: a fixed internal knowledge state that cannot reflect post-training developments and that carries a persistent risk of generating plausible yet factually unsupported content. Retrieval-Augmented Generation (RAG) offers a compelling remedy by coupling the generative capacity of LLMs with a dynamically queryable external knowledge store, thereby decoupling reasoning from memorisation. This work conducts a structured systematic review of RAG research spanning the period 2020 through 2026, charting its progression from rudimentary retrieve-then-read configurations toward sophisticated pipelines that incorporate modular retrieval components and autonomous agent-driven reasoning. Core technical mechanisms are analysed in depth, covering bi-encoder and late-interaction retrieval models, multi-passage fusion strategies, and the complementary roles of lexical and semantic search. Quantitative evidence drawn from widely adopted open-domain benchmarks confirms that retrieval-augmented systems consistently surpass purely parametric baselines on factual question-answering tasks. The review further examines how self-critique loops and structured knowledge graphs are being employed to reduce model hallucinations at scale. Concluding observations chart priority research directions in multimodal retrieval, temporal knowledge decay, and privacy-safe retrieval, positioning RAG as the foundational knowledge infrastructure for next-generation trustworthy AI deployments. Keywords Retrieval-Augmented Generation, Large Language Models, Dense Passage Retrieval, Knowledge Grounding, Agentic AI Systems, Semantic Vector Search