In this conversation, Stefan Webb (Developer Advocate) and Jay Byoun (Solutions Architect) discuss the intricacies of Zilliz Cloud and vector databases, focusing on indexing algorithms, recall and latency trade-offs, chunking strategies, cost considerations in LLM inference, and diverse use cases for vector databases. They also provide insights on how to get started with Zilliz, emphasizing the importance of understanding these concepts for effective data management and application development.

To learn more about working with unstructured data, visit ⁠zilliz.com⁠ and ⁠milvus.io⁠

In this episode, Stefan Webb (developer advocate at Zilliz) interviews Cameron Pfiffer from .txt, discussing the intersection of generative AI, developer advocacy, and structured output. Cameron shares his journey into the field, the mission of .txt, and the importance of constrained sampling in AI development. The conversation explores the functionalities of Outlines, a tool designed to enhance structured output, and its future developments. Additionally, they discuss the integration of vector databases with constrained output and how listeners can engage with the community and resources available.

To learn more about working with unstructured data, visit zilliz.com and ⁠milvus.io.

Stephen Batifol and Stefan Webb, Developer Advocates at Zilliz (the maintainers of the open source vector database Milvus), explore the increasingly important world of GenAI model evaluation. They discuss why it is important to evaluate your RAG or Agent pipeline during design and after deployment, how to do it, and where things are headed. Learn about the experimental mindset, LLM-as-a-Judge, RAGAS library, and other exciting topics!

To learn more about working with unstructured data, visit ⁠zilliz.com⁠ and ⁠milvus.io⁠.