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Data Management for Enterprise LLMs

2025/2/7

AI + a16z

AI Deep Dive AI Chapters Transcript

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Derek Harris

George Fraser

Guido Appenzeller

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George Fraser: 数据准备的核心在于从业务中获取上下文,理解数据的真实含义。我发现,数据准备不仅仅是技术问题,更多时候需要深入业务,与相关人员沟通,理解数据背后的逻辑和规则。例如,Salesforce 中两个字段有时会同时存在数值,需要理解其背后的业务规则。我认为,未来的解决方案可能需要一个 LLM 代理,能够主动提问,澄清数据含义,最终简化数据视图。数据准备的本质是创建一个简化的世界视图,掩盖原始数据集中那些公司特有的、难以理解的特性。这不仅仅是数据问题,很多时候需要推动组织变革,才能从根本上解决问题。 Guido Appenzeller: 我认为在大型企业中,对于“收入”一词可能有多种不同的定义,AI 尚未理解这些语义,因此仍需要人工参与。例如,企业内部不同部门,如销售、财务、税务等,对收入的定义和计算方式可能存在差异。AI 目前还无法理解这些细微的语义差别,因此在数据准备和分析过程中,仍然需要人工的参与和判断,以确保数据的准确性和一致性。

Deep Dive

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This chapter explores how generative AI, particularly LLMs, impacts enterprise data management. It highlights the increasing importance of handling unstructured text data and the potential for LLMs to improve enterprise search. The discussion emphasizes the importance of using existing data infrastructure for AI projects, rather than creating entirely new stacks.

Generative AI enables processing of unstructured text data.
LLMs enhance enterprise search capabilities.
Reusing existing data infrastructure is recommended for AI projects.

Shownotes Transcript

In this episode of AI + a16z, Fivetran) cofounder and CEO George Fraser and a16z partner Guido Appenzeller discuss how LLMs fit into the data management picture within large enterprises. In order to take advantage of a potentially revolutionary technology, organizations don't need to rip out their existing infrastructure, but they do need to rethink their data hygiene so language models can understand it.

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Data Management for Enterprise LLMs 38:12 Share

AI + a16z

Deep Dive

Shownotes Transcript

Data Management for Enterprise LLMs