Retrieval Augmented Generation for Large Language Models: A Methodological Survey

Over the past years, large language models (LLMs) have gained significant attention and have led to major breakthroughs in natural language processing (NLP). Despite their remarkable efficiency and contextual understanding, LLMs still remain limited by their tendency to generate hallucinated text, their reliance on static internal knowledge, and their limited interpretability. To address these limitations, retrieval-augmented generation (RAG) techniques have emerged as a hybrid architecture that couples the generative capabilities of LLMs with external knowledge retrieval processes. By drawing on external knowledge at inference time, RAG architectures are intended to enhance the accuracy and relevance of their generated output, and they have already been widely used in areas where extensive, detailed and up-to-date knowledge is needed. For this study, Scopus, IEEE Xplore, and the ACM Digital Library were explored and utilized, with Google Scholar used only for backward and forward citation checking, resulting in 191 peer-reviewed journal articles published between January 2024 and April 2026. This methodological review presents the research efforts regarding the use of RAG in LLMs for a variety of application fields. Additionally, this review summarizes the main RAG architectural approaches, deployment strategies, and evaluation metrics. Furthermore, the main open issues and future directions are examined, to motivate further research on the field.