The success of Generative AI applications depends on the quality of data and the continuous monitoring of model performance. This session will focus on the critical role of LLMOps in managing data pipelines, ensuring data integrity, and implementing effective monitoring strategies for Large Language Models (LLMs). Real-world examples, such as customer feedback analysis, will be highlighted to explore advanced techniques in data processing and analysis. These include leveraging Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG)workflows with tailored filters, utilizing visual analytics for exploratory data analysis, and tracking functional measurements through Function Calling Agents to evaluate model performance. The session will also cover the importance of establishing Human-in-the-Loop (HITL) processes to ensure continuous validation and improvement. Attendees will gain actionable insights into : → building a data-driven LLMOps framework, empowering their generative AI initiatives to deliver sustained value through continuous improvement.