Retrieval Augmented Generation (RAG) with Data Streaming
Key Takeaways
⇨ LLMs require real-time, contextualized, and trustworthy data to minimize hallucinations and ensure reliable outputs.
⇨ The use of Retrieval-Augmented Generation (RAG) enhances the performance of LLMs by integrating dynamic data sources.
⇨ A data streaming platform like Apache Kafka and Flink is essential for providing the necessary infrastructure to deliver timely and accurate data to LLMs.
How do you prevent hallucinations from large language models (LLMs) in GenAI applications? LLMs need real-time, contextualized, and trustworthy data to generate the most reliable outputs. Kai Waehner, Global Field CTO at Confluent, explains how RAG and a data streaming platform with Apache Kafka and Flink make that possible. Watch Video ...
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