Democratizing AI - Vector Databases and LLM Applications

Explore the world of vector databases and their role in democratizing AI in this 54-minute podcast episode featuring Yujian Tang, Developer Advocate at Zilliz. Dive into the importance of vector databases like Milvus in production environments and how they address data challenges in building large language model (LLM) applications. Learn about Zilliz's efforts to democratize vector databases through education, expanded access to technologies, and technical evangelism. Gain insights into enterprise use cases, common pitfalls in generative AI apps, and when to use or avoid vector databases. Discover the trajectory of vector databases, trade-offs, difficulties in training LLMs, and the significance of understanding your data. The discussion also covers topics such as Elastic search, data mesh, and real-world enterprise scenarios, providing a comprehensive overview of the current state and future potential of AI democratization.

[] Yujian's preferred coffee
[] Takeaways
[] Please share this episode with your friends!
[] Vector databases trajectory
[] 2 start-up companies created by Yujian
[] Uninitiated Vector Databases
[] Vector Databases trade-off
[] Difficulties in training LLMs
[] Enterprise use cases
[] Process/rules not to use LLMs unless necessary
[] Setting up returns
[] When not to use Vector Databases
[] Elastic search
[] Generative AI apps common pitfalls
[] Knowing your data
[] Milvus
[] Actual Enterprise use cases
[] Horror stories
[] Data mesh
[] GPTCash
[] Shout out to the Seattle Community!
[] Wrap up