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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.
Britton LaRoche, Databricks
Vasanth Kumar, MongoDB
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.
Mac Khajuria, Confluent
Erick Lee, Confluent
Get started with the basics of LLMs, vector databases, and AI.
More Generative AI Resources
A Guide to Event-Driven Design for Agents and Multi-Agent Systems
A data streaming platform enables AI agents to operate dynamically, processing and acting on real-time events. Learn the anatomy of an agent, how to build singular to multi-agent systems, design patterns for scalable architectures, and real-world agentic AI applications.
The Developer’s Guide to RAG with Data Streaming
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.
Powering AI Agents with Anthropic’s MCP and Confluent
Learn how Anthropic’s Model Context Protocol (MCP) and Confluent Data Streaming Platform enable AI agents to operate on real-time data and scale across enterprise environments. The demo tutorial shows how to implement MCP Server and use Claude LLM with connectors, Flink, and Stream Governance to interact with data in natural language.
Building the Real-Time Backbone for Agentic AI with Confluent and Google Cloud
Unlock the full potential of Generative AI with real-time data. Join Confluent and Google Cloud to learn how to build intelligent, event-driven agents using A2A, MCP, and a real-time backbone powered by Confluent and Google Cloud AI.
AI-Powered Personalization with Oracle XStream CDC Connector
Explore a demo of the Oracle XStream CDC Source Connector—a high-performance solution that transforms your approach to real-time AI personalization. Stream database changes faster, build AI engines that respond instantly to customer interactions, and set up multi-channel experiences in real time.
Harnessing real-time data for generative AI with Confluent and AWS
Relying on stale, disparate data from across your systems can impact AI model accuracy and development. Confluent offers fully managed data streaming capabilities to stream, connect, process and govern data for business critical applications.
See how you can integrate real-time data streams with AWS services, such as Amazon Bedrock and Amazon SageMaker, in this session. Gen AI experts will walk through real world use cases and share valuable strategies for improving your AI applications.
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 Event-Driven Enterprise AI Agents with AutoGen and Confluent
How to Build Event-Driven Enterprise AI Agents with AutoGen and Confluent
Agentic AI is transforming enterprise operations with autonomous problem-solving, adaptive workflows, and scalability. Learn how to build real-time multi-agents using AutoGen, Azure OpenAI's GPT LLM, MongoDB, Confluent and Flink.