A Survey of Retrieval-Augmented Generation (RAG) for Large Language Models

Abstract

While Large Language Models (LLMs) are revolutionary, their deployment is constrained by inherent limitations such as factual hallucination and static knowledge. This survey systematically reviews Retrieval-Augmented Generation (RAG), a key paradigm for addressing these challenges by grounding LLMs in external, verifiable knowledge. To overcome the flaws of standalone models, RAG integrates LLMs with updatable knowledge bases, a hybrid approach that significantly enhances output accuracy and trustworthiness. Our primary finding is the technology’s clear evolutionary trajectory, which we structure into three stages: Naive, Advanced, and Modular RAG. This progression demonstrates a shift away from monolithic parametric memory towards intelligent systems that interact with external data. By summarizing the field’s progression, key challenges like retriever-generator alignment, and future directions such as integration with agentic architectures, this work concludes that RAG is a crucial technology for propelling AI to be more evidence-based and capable of complex reasoning.

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