The article discusses the challenges of data preparation for AI model accuracy and proposes a solution centered around executing data-related operations 10x faster. The focus is on the messiest part of the problem: data annotation and labeling. The solution leverages the zero-shot capability of LLMs and proprietary algorithms to create a prompt-based interface that simplifies the labeling workflow and reduces costs and complexity. The system generates results quickly, flags where attention is needed, and allows for fine-tuning labels and outsourcing subjective cases. The benefit is a 90%+ reduction in time spent on data-related workflows. The author invites readers to connect on LinkedIn to discuss aspects of computer vision data preparation, data ops, and pipelines.