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Generative AI is already changing the way we work, but how can we change the way GenAI works? See where large language models (LLMs) and GenAI are today and where better data can take them tomorrow.
In this GenAI tutorial webinar by Confluent and MongoDB, you’ll learn how to build retrieval-augmented generation (RAG) in 4 key steps: data augmentation, inference, workflows, and post-processing. See a step-by-step walkthrough of vector embedding and get all your questions answered in a live Q&A.
In this demo webinar, you’ll learn about building a real-time knowledge base for RAG architecture. We’ll show you how to configure a source connector to bring in data from various sources, Flink SQL for vector embedding, and sink connector to send data to vector stores like MongoDB Atlas Vector search.
How to effectively build and scale retrieval-augmented generation (RAG) for real-time Generative AI applications using Confluent’s Data Streaming Platform. Leverage connectors, Flink, and stream governance across 4 steps: data augmentation, inference, workflows, and post-processing.
In this demo webinar, you’ll learn about building a real-time knowledge base for RAG architecture. We’ll show you how to configure a source connector to bring in data from various sources, Flink SQL for vector embedding, and sink connector to send data to vector stores like MongoDB Atlas Vector search.
In this GenAI webinar by Confluent and Elastic, learn how to build an AI chatbot that uses retrieval-augmented generation (RAG) for accurate and contextually aware responses. We'll demo step by step through a real-world use case: Finserv document search and synthesis.
Join our webinar to learn how Confluent and AWS can help you build and scale GenAI applications with ease. Discover event-driven microservices for GenAI, the importance of data integrity with Stream Governance, and how to leverage real-time data with Amazon Bedrock. Includes a live AI chatbot demo!
In this GenAI tutorial webinar by Confluent and MongoDB, you’ll learn how to build retrieval-augmented generation (RAG) in 4 key steps: data augmentation, inference, workflows, and post-processing. See a step-by-step walkthrough of vector embedding and get all your questions answered in a live Q&A.