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Event-Driven AI: Supercharging ChatGPT with RAG & LangStream

Large Language Models like ChatGPT are fantastic for many NLP tasks but face challenges when it comes to real-time, up-to-date knowledge retrieval. Retrieval Augmented Generation (RAG) can effectively tackle this by pulling in external data for better, more context-aware responses. This talk dives deep into using event-driven streaming through LangStream—an open-source library—to seamlessly integrate real-time data into generative AI applications like ChatGPT. Walk away with actionable insights on how to boost your AI applications using event streaming and RAG.

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About the speaker

Mary Grygleski

Java Champion & Senior Developer Advocate, DataStax

Mary is a Java Champion and a passionate Developer Advocate at DataStax, a leading data management company that champions Open Source software and specializes in Real-Time AI, Big Data, DB-as-a-Service, Streaming, and Cloud-Native systems.  With over 25 years of hands-on experience in the software engineering and technical architecture areas, she began venturing into developer advocacy in 2018 at IBM, and has never looked back since then.  She enjoys reaching out to developers and helping them succeed in their work.  Outside of her day job, she is an active tech community builder, and currently the President of the Chicago Java Users Group (CJUG).