An Analysis of Retrieval-Augmented Generation: A Systematic Review Addressing Architectures, Components, and Evaluation
Abstract
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external retrieval mechanisms to improve factuality and currency. This systematic literature review characterizes current RAG architectures, components, and evaluation practices in peer-reviewed studies published between 2021 and 2025 across IEEE Xplore, Scopus, and Web of Science. Conducted in accordance with the PRISMA guidelines, this review analyzes 41 studies that met the predefined inclusion criteria. Most research addresses Question Answering (QA) and dialogue systems, employing diverse encoders and retrieval optimization methods. Key findings reveal a strong trend toward integrating OpenAI’s GPT models, alongside growing adoption of open-source alternatives. Persistent challenges include hallucination control, computational efficiency, and inconsistent evaluation metrics. Despite the potential of RAG, the evidence base is limited by a focus on English-language, high-resource domains. Furthermore, reproducibility is constrained by heterogeneous evaluation standards and a lack of open-access code or datasets. This review maps the RAG research landscape and identifies gaps in standardization, scalability, and application to low-resource languages. The protocol was not prospectively registered, and no funding was received for this work.
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