Retrieval-Augmented Generation (RAG) architecture is emerging as a crucial solution for improving the accuracy and timeliness of large language models (LLMs) like GPT-4 and Claude, particularly in knowledge-intensive fields like healthcare, law, and finance. RAG combines a pretrained LLM (parametric memory) with an external database (non-parametric memory) to provide real-time retrieval of relevant information, addressing the limitations of fixed knowledge in traditional LLMs. This approach allows models to incorporate up-to-date data without retraining, thereby reducing inaccuracies and hallucinations, which are common issues with standalone LLMs. The integration of RAG is reshaping enterprise operations, with companies increasingly adopting it to enhance AI capabilities across various business functions, as evidenced by a significant rise in AI usage reported by McKinsey. Open-source frameworks such as Haystack, LangChain, and LlamaIndex facilitate the deployment of RAG systems, which are not only efficient but also allow for dynamic updates to external knowledge bases. As RAG systems become more prevalent, they offer a scalable and reliable solution for organizations aiming to leverage AI while ensuring compliance and reducing error rates.