Episode
Expert Insights On Retrieval Augmented Generation And How To Build It
- Podcast
- AI Engineering Podcast
- Published
- Jul 28, 2024
- Duration seconds
- 3801
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Summary
Summary In this episode we're joined by Matt Zeiler, founder and CEO of Clarifai, as he dives into the technical aspects of retrieval augmented generation (RAG). From his journey into AI at the University of Toronto to founding one of the first deep learning AI companies, Matt shares his insights on the evolution of neural networks and generative models over the last 15 years. He explains how RAG addresses issues with large language models, including data staleness and hallucinations, by providing dynamic access to information through vector databases and embedding models. Throughout the conversation, Matt and host Tobias Macy discuss everything from architectural requirements to operational considerations, as well as the practical applications of RAG in industries like intelligence, healthcare, and finance. Tune in for a comprehensive look at RAG and its future trends in AI. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Your host is Tobias Macey and today I'm interviewing Matt Zeiler, Founder & CEO of Clarifai, about the technical aspects of RAG, including the architectural requirements, edge cases, and evolutionary characteristics Interview Introduction How did you get involved in the area of data management? Can you describe what RAG (Retrieval Augmented Generation) is? What are the contexts in which you would want to use RAG? What are the alternatives to RAG? What are the architectural/technical components that are required for production grade RAG? Getting a quick proof-of-concept working for RAG is fairly straightforward. What are the failures modes/edge cases that start to surface as you scale the usage and complexity? The first step of building the corpus f…