Weaviate Podcast

Weaviate
Weaviate Podcast

Join Connor Shorten as he interviews machine learning experts and explores Weaviate use cases from users and customers.

  1. 6 DAYS AGO

    Contextual AI with Amanpreet Singh - Weaviate Podcast #114!

    Hey everyone! Thank you so much for watching the 114th episode of the Weaviate Podcast featuring Amanpreet Singh, Co-Founder and CTO of Contextual AI! Contextual AI is at the forefront of production-grade RAG agents! I learned so much from this conversation! We began by discussing the vision of RAG 2.0, jointly optimizing generative and retrieval models! This then lead us to discuss Agentic RAG and how the RAG 2.0 roadmap is evolving with emerging perspectives on tool use. Amanpreet continues to further motivate the importance of continual learning of the model and the prompt / few-shot examples -- discussing the limits of prompt engineering. Personally I have to admit I think I have been a bit too bullish on only tuning instructions / examples, Amanpreet made an excellent case for updating the weights of the models as well -- citing issues such as parametric knowledge conflicts, and later on discussing how Mechanistic Interpretability is used to audit models and their updates in enterprise settings. We then discussed Contextual AI's LMUnit for evaluating these systems. This then lead us into my favorite part of the podcast, a deep dive into RL algorithms for LLMs. I highly recommend checking out the links below to learn more about Contextual's innovations on APO and KTO! We then discuss the importance of domain specific data, Mechanistic Interpretability, return to another question on RAG 2.0, and conclude with Amanpreet's most exciting future directions for AI! I hope you enjoy the podcast!

    58 min
  2. JAN 15

    Google Vertex AI RAG Engine with Lewis Liu and Bob van Luijt - Weaviate Podcast #112!

    Hey everyone! Thank you so much for watching the 112th episode of the Weaviate Podcast! This is another super exciting one, diving into the release of the Vertex AI RAG Engine, its integration with Weaviate and thoughts on the future of connecting AI systems with knowledge sources! The podcast begins by reflecting on Bob's experience speaking at Google in 2016 on Knowledge Graphs! This transitions into discussing the evolution of knowledge representation perspectives and things like the semantic web, ontologies, search indexes, and data warehouses. This then leads to discussing how much knowledge is encoded in the prompts themselves and the resurrection of rule-based systems with LLMs! The podcast transitions back to topics around the modern consensus in RAG pipeline engineering. Lewis suggests that parsing in data ingestion is the biggest bottleneck and low hanging fruit to fix. Bob presents the re-indexing problem and how it is additionally complicated with embedding models! Discussing the state of knowledge representation systems inspired me to ask Bob further about his vision with Generative Feedback Loops and controlling databases with LLMs, How open ended will this be? We then discuss the role that Agentic Architectures and Compound AI Systems are having on the state of AI. What is the right way to connect prompts with other prompts, external tools, and agents? The podcast then concludes by discussing a really interesting emerging pattern in the deployment of RAG systems. Whereas the first generation of RAG systems typically were user facing, such as customer support chatbots, the next generation is more API-based. The launch of the Vertex AI RAG Engine quickly shows you how to use RAG Engine as a tool for a Gemini Agent!

    58 min
4
out of 5
4 Ratings

About

Join Connor Shorten as he interviews machine learning experts and explores Weaviate use cases from users and customers.

You Might Also Like

To listen to explicit episodes, sign in.

Stay up to date with this show

Sign in or sign up to follow shows, save episodes, and get the latest updates.

Select a country or region

Africa, Middle East, and India

Asia Pacific

Europe

Latin America and the Caribbean

The United States and Canada