Large Language Models (LLMs) have transformed the way engineering teams manage and analyze data, but integrating and evaluating these systems for production remains challenging. Chalk offers a comprehensive data platform designed to streamline LLM workflows by enabling prompt development, evaluation, and deployment within a single interface, reducing the need for multiple tools. This post illustrates Chalk's capabilities through a Christopher Nolan trivia challenge, demonstrating how to define, test, and evaluate prompts efficiently. Chalk supports LLMs as integral parts of the machine learning stack, providing a unified interface for inference and prompt evaluation, which includes tracking performance metrics like token usage and latency. By simplifying the process of dataset creation, prompt definition, and large-scale evaluation, Chalk allows teams to focus on innovation while ensuring reliable and reproducible results. The successful deployment of the best-performing model, Claude Sonnet 4, highlights Chalk's ability to facilitate production-ready LLM features, emphasizing the importance of structured prompt engineering and native evaluation for scalable and efficient ML operations.