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The success of any generative AI system is inseparable from the quality, structure, and integrity of the data that feeds it. Whether the objective is to generate text, images, video, audio, simulations, or multimodal outputs, every capability originates from data. Data is not only the material generative models are trained on; it is also the medium through which these models express their learned understanding of the world. In generative AI, the data defines the edges of what is possible—what can be learned, what can be synthesized, and ultimately, what can be trusted.

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Data Collection and Preparation

  • 章节

  • 发表日期：

    2026年

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  • Irena Cronin

摘要

The success of any generative AI system is inseparable from the quality, structure, and integrity of the data that feeds it. Whether the objective is to generate text, images, video, audio, simulations, or multimodal outputs, every capability originates from data. Data is not only the material generative models are trained on; it is also the medium through which these models express their learned understanding of the world. In generative AI, the data defines the edges of what is possible—what can be learned, what can be synthesized, and ultimately, what can be trusted.