Retrieval-Augmented Generation (RAG) systems represent a critical advancement in the enhancement of Large Language Models (LLMs) by integrating dynamic data retrieval mechanisms. Unlike traditional LLMs, which rely exclusively on pre-trained parameters, RAG architectures enable models to access and incorporate external, real-time information. This integration is particularly advantageous for applications requiring