The text discusses how leveraging large language models (LLMs) like GPT can significantly streamline the process of data labeling, which traditionally required extensive machine learning expertise and large datasets. The application of LLMs via carefully crafted prompts allows for efficient categorization of data such as product reviews, GitHub issues, and emails, among others. The text suggests using "functions" in GPT to ensure precise output, and emphasizes the importance of including justifications for categorization decisions to facilitate debugging and understanding. It also advises having a failure category to handle uncertain data and suggests a "waterfall" approach by escalating difficult cases to more advanced models. Additionally, the document warns against defining too many categories in a single prompt to prevent confusion. The mention of AgentHub highlights a platform that offers no-code automated data labeling solutions, simplifying the process for users by providing prebuilt tools like the 'Categorizer' node to efficiently manage large-scale data processing tasks.