Retrieval-Augmented Generation (RAG) Systems: Architectures, Strategies, and Evaluation
Abstract
Retrieval-Augmented Generation (RAG) systems have revolutionized the way large language models (LLMs) synthesize responses by coupling generative capabilities with dynamic retrieval from external knowledge bases. This integration not only enhances the factual accuracy and contextual relevance of responses but also reduces the potential for hallucination in generated content. In this paper, we present an extensive survey of RAG systems, covering theoretical underpinnings, various retrieval strategies, agentic architectures, and the technical developer stack necessary for system integration. Additionally, we detail advanced techniques for fine-tuning embedding and reranker models and establish comprehensive evaluation metrics applicable to both retrieval and generation components. This document synthesizes methods and best practices described in recent research articles [1]–[10], offering a roadmap for researchers and practitioners to design robust, efficient, and scalable RAG systems.
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