A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges

2025-08-08

AI Summary

This study addresses the lack of systematic, up-to-date synthesis of retrieval-augmented generation (RAG) research amid rapid methodological diversification and evaluation fragmentation. Adopting the PRISMA 2020 framework, we systematically curated 128 highly cited papers (2020–2025) from ACM, IEEE, and other authoritative sources, introducing a dynamic citation threshold to mitigate temporal bias. Our analysis maps evolutionary trajectories across three dimensions: architectural design, benchmark datasets, and evaluation metrics—revealing critical methodological gaps, particularly in non-parametric memory augmentation and neural retrieval–generation co-adaptation. We construct a structured knowledge graph of RAG research and propose a prioritized roadmap that jointly optimizes robustness, interpretability, and generalization. The findings provide empirically grounded guidance for both foundational RAG theory development and practical deployment.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This systematic review of the research literature on retrieval-augmented generation (RAG) provides a focused analysis of the most highly cited studies published between 2020 and May 2025. A total of 128 articles met our inclusion criteria. The records were retrieved from ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and the Digital Bibliography and Library Project (DBLP). RAG couples a neural retriever with a generative language model, grounding output in up-to-date, non-parametric memory while retaining the semantic generalisation stored in model weights. Guided by the PRISMA 2020 framework, we (i) specify explicit inclusion and exclusion criteria based on citation count and research questions, (ii) catalogue datasets, architectures, and evaluation practices, and (iii) synthesise empirical evidence on the effectiveness and limitations of RAG. To mitigate citation-lag bias, we applied a lower citation-count threshold to papers published in 2025 so that emerging breakthroughs with naturally fewer citations were still captured. This review clarifies the current research landscape, highlights methodological gaps, and charts priority directions for future research.

Problem

• Analyzes retrieval-augmented generation techniques and challenges
• Evaluates datasets, architectures, and metrics in RAG studies
• Identifies research gaps and future directions for RAG

Innovation

• Neural retriever coupled with generative model
• PRISMA 2020 framework guided analysis
• Lower citation threshold for recent breakthroughs