Build Predictive Machine Learning with Flink | Workshop on Dec 18 | Register Now
GenAI is here, but where’s it going next?
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.
Here’s what’s trending in artificial intelligence now.
How to Build RAG Using Confluent with Flink AI Model Inference and MongoDB
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.
How to Build GenAI apps Faster with Confluent’s Connectors
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.
Get started with the basics of LLMs, vector databases, and AI.
More Generative AI Resources
How to Build a Real-Time RAG-Enabled AI Chatbot with Flink, Elastic, OpenAI, and LangChain
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.
Build Your GenAI Stack with Confluent and AWS for Real-Time Use Cases
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!
How to Build RAG Using Confluent with Flink AI Model Inference and MongoDB
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.
Building Real-Time, Intelligent AI Copilots with Confluent Cloud, Azure OpenAI & Azure Data Services
Join our webinar to explore how Confluent's data streaming platform, Azure data services, and Azure OpenAI deliver real-time insights and help customers conduct seamless business transactions.