AI is revolutionizing business operations, but challenges like hallucinations in large language models (LLMs) remain due to their reliance on static datasets. Retrieval-Augmented Generation (RAG) is a method gaining traction to mitigate these issues by providing LLMs with real-time access to domain-specific data, thereby enhancing response accuracy. RAG involves two main phases: data preparation, which organizes and embeds unstructured data into a vector database, and retrieval and generation, where relevant data is fetched to generate accurate responses. Despite its benefits, RAG implementation can be complex and time-consuming, prompting solutions like the Kong AI Gateway to automate and streamline this process, ensuring consistent, secure, and high-quality AI outputs. This approach is particularly vital in sectors such as healthcare, law, and finance, where precise information is critical, and allows organizations to operationalize AI strategies more effectively by reducing errors and improving governance.