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Data tagging and annotation are essential components of generative AI, enabling machines to learn and generate new data based on existing patterns and structures. With the help of data tagging and annotation, generative AI can learn from large volumes of unstructured data, recognize patterns, and generate new data that follows the same patterns. What Are Data Tagging And Annotation? Data tagging and annotation are processes of labeling and categorizing data to provide contextual information for machine learning models. It involves adding metadata, such as keywords, labels, or notes, to data, making it easier for machines to understand and analyze. Data tagging and annotation are used in various industries, including finance, healthcare, e-commerce, and entertainment, to improve searchability, enhance user experience, and streamline business processes. Functionalities Of Data Tagging And Annotation In Generative AI: In Generative AI, data tagging and annotation play crucial roles in im