Vector Database founder: The problem with RAG terminology I Jeff Huber from Chroma

This week on High Agency, Raza Habib is joined by Chroma founder Jeff Huber. They cover the evolution of vector databases in AI engineering, challenge common assumptions about RAG and share insights from Chroma's journey. Jeff shares insights from Chroma's development, including their focus on developer experience and observations about real-world usage patterns. They also get into whether or not we can expect a super AI any time soon and what is over and under hyped in the industry today.

00:00 - Introduction
02:30 - Why vector databases matter for AI
06:00 - Understanding embeddings and similarity search
12:00 - Chroma early days
15:45 - Problems with existing vector database solutions
19:30 - Workload patterns in AI applications
23:40 - Real-world use cases and search applications
27:15 - The problem with RAG terminology
31:45 - Dynamic retrieval and model interactions
35:30 - Email processing and instruction management
39:15 - Context windows vs vector databases
42:30 - Enterprise adoption and production systems
45:45 - The journey from GPT-3 to production AI
48:15 - Internal vs customer-facing applications
51:00 - Advice for AI engineers

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