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