[Demo] Design Event-Driven Microservices for Cloud → Register Now

Online Talk

Data Streaming and Retrieval-Augmented Generation (RAG) for Generative AI

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Available On-demand

Is your AI chatbot hallucinating? LLMs are a great foundational tool that has made AI accessible for everyone, but they lack real-time domain-specific data. Building cutting-edge GenAI applications requires an understanding of context around a query and generating relevant, accurate results.

This is where RAG comes in. RAG is a pattern that pairs prompts with real-time external data to improve LLM responses.

Join Confluent experts Andrew Sellers, Head of Technology Strategy, and Kai Waehner, Global Field CTO, as they deep dive into RAG and the 4 Steps for Building Event-Driven GenAI Applications. Register now to learn:

  • How to build a real-time, contextualized, and trustworthy knowledge base
  • Where a data streaming platform and Apache Flink® stream processing (with AI model inference) fit in the RAG architecture
  • Key steps of data augmentation, inference, workflows, and post-processing
  • How a RAG demo works, featuring an AI chatbot that provides personalized product recommendations—built using Confluent, OpenAI, ChatGPT-4, Flink, MongoDB, and D-ID

Andrew Sellers leads Confluent’s Technology Strategy Group, a team supporting strategy development, competitive analysis, and thought leadership.

Kai Waehner is Field CTO at Confluent. He works with customers across the globe and with internal teams like engineering and marketing. Kai’s main area of expertise lies within the fields of Data Streaming, Analytics, Hybrid Cloud Architectures, Internet of Things, and Blockchain. Kai is a regular speaker at international conferences such as Devoxx, ApacheCon and Kafka Summit, writes articles for professional journals, and shares his experiences with new technologies on his blog: www.kai-waehner.de. Contact: kai.waehner@confluent.io / @KaiWaehner / linkedin.com/in/kaiwaehner.

Additional Resources

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Confluent Cloud Demo

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Kafka Microservices

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e-book: Microservices Customer Stories

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