Generative AI (GenAI) has garnered significant attention for its potential to revolutionize various industries, from creative arts to data analysis. However, organizations are realizing that implementing GenAI is not as easy as just asking ChatGPT a few questions. Providing the most relevant and accurate contextual data to the LLM is critical if organizations are going to realize the full benefits of GenAI. Retrieval Augmented Generation, or RAG, is a well understood and effective technique for augmenting the original user prompt with additional, contextual data. However, many examples of RAG grossly oversimplify the reality of enterprise data ecosystems. In this session, we will examine how a Logical Data Fabric can make RAG a practical reality in large, complex organizations and deliver AI-ready data that make RAG effective and accurate.
talk-data.com
Speaker
Paul Moxon
1
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Paul is Denodo’s VP of Data Architecture and Chief Evangelist and he works closely with Denodo’s customers to understand their use of Data Virtualization and recommending usage patterns, deployment architectures, and best practices for using the Denodo Platform. Paul has over 30 years of experience in the software industry, starting as a developer building real-time control systems for oil and gas pipelines in the UK and the Netherlands before working as a consultant on large integration projects with the likes of AT&T Wireless, Bechtel Corporation, Saudi Aramco, and Reuters. He has also worked for leading software companies such as BEA Systems, Progress Corporation, and IONA Technologies.
Bio from: Big Data LDN 2024
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