Vanilla Retrieval-Augmented Generation (RAG) is becoming more and more adopted - but What is next? Join us for an introduction to Generative Feedback Loops (GFL). GFLs can level-up your RAG architecture by generating more insights on top of your data. This empowers you to implement smarter chatbots, agents, and other AI-driven solutions. Based on hands-on examples, we will explore the following questions: a) What are GFLs are? b) How do GFLs work? c) What challenges and use-cases can we tackle with GFLs; and d) How can I define my AI workflows to implement GFLs. Learn about the next level of AI applications and join us for a Live Demo.

In this session, we'll discuss how data is stored, retrieved, augmented and isolated for users, and how index types, quantization, multi-tenancy, sharding, and replication affect their behaviour and performance. We will also discuss vector databases' integration with AI models that can generate vectors, or use retrieved data to produce augmented, or transformed outputs. When you emerge from this deep dive, you will have seen the inner workings of a vector database, and the key aspects that make them different to your grandma's database.

In this talk, we are going to explore how vector databases like Weaviate can be used to build the next level of RAG applications. Based on examples we are looking at the possibilities of RAG architectures that can handle multimodal context like images, videos and much more and how vector databases work in such a setting