Generative AI and Retrieval-Augmented Generation (RAG) Systems for Enterprise

This workshop introduces generative AI applications for enterprise, with a focus on retrieval-augmented generation (RAG) systems. Generative AI is a field of artificial intelligence that can create new content and solve complex problems. RAG systems are a novel generative AI technique that combines information retrieval with text generation to generate rich and diverse responses. RAG systems can leverage enterprise data, which is often specific, structured, and dynamic, to provide customized solutions for various domains. However, enterprise data also poses challenges such as scalability, security, and data quality. This workshop convenes researchers and practitioners to explore RAG and other generative AI systems in real-world enterprise scenarios, fostering knowledge exchange, collaboration, and identification of future directions. Relevant to the CIKM community, the workshop intersects with core areas of data science and machine learning, offering potential benefits across various domains.

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Publication Details

Published
2024-10-20
DOI
https://doi.org/10.1145/3627673.3680117
Citations
5
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.64
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article

Generative AI and Retrieval-Augmented Generation (RAG) Systems for Enterprise

Pritam Gundecha, Anbang Xu, Xinyun Chen, Tan Yu et al.
5 citations
Big Data and Business Intelligence
2.64
article

Generative AI and Retrieval-Augmented Generation (RAG) Systems for Enterprise

Pritam Gundecha, Anbang Xu, Xinyun Chen, Tan Yu, Ping Li, Xinliang Zhu, Min Du, Yufan Guo, May Wang
article en
5 citations

Abstract

This workshop introduces generative AI applications for enterprise, with a focus on retrieval-augmented generation (RAG) systems. Generative AI is a field of artificial intelligence that can create new content and solve complex problems. RAG systems are a novel generative AI technique that combines information retrieval with text generation to generate rich and diverse responses. RAG systems can leverage enterprise data, which is often specific, structured, and dynamic, to provide customized solutions for various domains. However, enterprise data also poses challenges such as scalability, security, and data quality. This workshop convenes researchers and practitioners to explore RAG and other generative AI systems in real-world enterprise scenarios, fostering knowledge exchange, collaboration, and identification of future directions. Relevant to the CIKM community, the workshop intersects with core areas of data science and machine learning, offering potential benefits across various domains.

Google (United States) (US), Amazon (United States) (US), Palo Alto Networks (United States) (US), Visual Editor Consultants (United States) (US), Nvidia (United States) (US)
Openalex Percentile: Top 8%
Big Data and Business Intelligence
2.64
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Generative AI and Retrieval-Augmented Generation (RAG) Systems for Enterprise — Pritam Gundecha, Anbang Xu, et al. · (2024) | TGRS Research Map | TGRS