Promptim is an open-source library designed to automate and enhance prompt engineering by refining AI prompts to achieve better performance on specific tasks. It uses a systematic optimization process that includes dataset input, custom evaluators, and optional human feedback to iteratively improve prompt efficacy. The library emphasizes the importance of evaluation-driven development, where AI engineers develop datasets and metrics to measure prompt performance. While Promptim focuses on optimizing individual prompts with human involvement in the loop, it contrasts with tools like DSPy that aim to optimize entire AI systems and offer broader solutions. Promptim integrates with LangSmith for managing datasets and tracking results, and its current development focuses on refining single prompts before considering wider system optimizations. Although not a comprehensive solution on its own, Promptim provides a more structured approach to prompt engineering, helping to save time, bring rigor, and facilitate model swapping. Future developments include dynamic few-shot prompting and more integration with LangSmith's UI.