Review on RAG-Powered LLM Architectures for Efficient Information Retrieval in Big Data Applications

Manish Adawadkar

Abstract:

The exponential growth of digital information in recent years has led
to the emergence of Big Data, which presents immense challenges in
terms of information retrieval (IR), knowledge integration, and
contextual understanding. Traditional retrieval methods, though
effective for structured data, struggle with scalability and semantic
reasoning in large and unstructured datasets. The development of
Large Language Models (LLMs) has revolutionized natural language
understanding, but their reliance on static knowledge and limited
contextual adaptability has exposed inherent limitations. To
overcome these challenges, the integration of Retrieval-Augmented
Generation (RAG) architectures has emerged as a powerful
paradigm. RAG combines the reasoning and generation capabilities
of LLMs with dynamic knowledge retrieval mechanisms to enhance
factual accuracy, contextual coherence, and adaptability. This paper
presents a comprehensive review of RAG-powered LLM
architectures, highlighting their underlying mechanisms, key design
components, optimization strategies, and applications across big data
ecosystems. Furthermore, it explores existing limitations, research
gaps, and future research opportunities for developing scalable,
explainable, and efficient retrieval-augmented intelligence systems.