Abstract

The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has transformed natural language processing applications across diverse domains. However, traditional language models often suffer from limitations such as hallucination, outdated knowledge, and lack of transparency in decision-making. Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm that combines information retrieval techniques with generative models to enhance factual accuracy, contextual relevance, and knowledge grounding. This survey presents a comprehensive review of the evolution of RAG-related technologies over the past two decades, from early information retrieval and question-answering systems to modern neural retrieval and large language model architectures. We examine the fundamental components of RAG frameworks, including retrieval mechanisms, embedding models, vector databases, reranking strategies, and generation modules. Furthermore, we compare prominent RAG architectures, discuss their strengths and limitations, and analyze their applications in healthcare, education, enterprise knowledge management, scientific research, and conversational AI[1-2]. Finally, we identify current challenges and future research directions, including multimodal retrieval, adaptive retrieval strategies, continual learning, and trustworthy AI systems. This survey aims to provide researchers and practitioners with a structured understanding of the development, state-of-the-art methods, and future prospects of Retrieval-Augmented Generation systems.