In a recent webinar, panelists discussed the growing importance of real-time Large Language Models (LLM) applications and their practical use cases in handling inventory, product, and review data. SingleStoreDB was highlighted as an ideal database for providing scalable, cost-effective, and performant storage and querying of large datasets, including structured and unstructured data and vectors. The platform's compatibility with popular technologies like Kafka, Spark, and Hadoop, as well as its deployment options across major cloud platforms, makes it a flexible solution for various use cases. Additionally, advancements in vector support and semantic searches, Neum AI's role in LLM application development, the significance of Retrieval Augmented Generation (RAG) in improving LLM responses, and the challenges involved in embedding data were discussed. These topics are crucial for developing and deploying successful LLM applications that can provide accurate and relevant information to users.