AutoPrompt is a prompt optimization framework that automatically generates and refines prompts for real-world LLM use cases. It employs an iterative calibration process that builds datasets of challenging edge cases and optimizes prompts accordingly, addressing issues like prompt sensitivity and ambiguity. The framework integrates with LangChain, Wandb, and Argilla, and supports applications including synthetic dataset generation and prompt migration across different models.