Retrieval-Augmented Generation (RAG) is emerging as a pivotal technology in enhancing artificial intelligence (AI) responses by incorporating real-time, relevant data into queries. This approach not only improves the coherence of AI outputs but also enriches them with contextual information. Industry leaders, including Intel, have introduced RAG solutions that are production-ready and built on the Open Platform for Enterprise AI (OPEA). These innovations aim to address the challenges enterprises face in deploying and scaling AI systems effectively. Additionally, tools like RAG Workbench have been developed to assist organizations in evaluating and trusting these AI systems in production environments. The integration of RAG is seen as essential for enterprises looking to implement successful AI solutions while mitigating issues related to inaccuracies in large language models (LLMs).