Retrieval-Augmented Generation (RAG): Advances and Challenges

The growing reliance on Large Language Models (LLMs) in knowledge-intensive tasks has led to the rapid adoption of Retrieval-Augmented Generation (RAG) as a strategy for improving factual grounding and domain adaptability.This review traces the evolution of RAG systems, from their roots in Information Retrieval (IR) and early Natural Language Processing (NLP) to current modular architectures that support dynamic reasoning and real-time knowledge integration.It categorizes key frameworks according to the specific challenges they target -such as retrieval precision, hallucination reduction, domain specialization, and interpretability -and analyzes how each addresses recurring failure modes in real-world applications.Through a comparative lens, the paper highlights both the fragmented nature of current solutions and the need for more unified, self-aware designs.Evaluation frameworks, including RAGAS, RGB, and PaSSER, are discussed in light of these gaps.Based on this analysis, the review outlines core directions for future research, emphasizing the importance of real-time retrieval validation, sentence-level attribution, failure correction mechanisms, and adaptable query rewriting.The findings suggest that RAG research is entering a phase of consolidation, where system reliability, transparency, and domain robustness will define progress more than generative fluency alone.