Promptim

Promptim is an experimental, yet powerful, library designed to systematically enhance AI system performance through automated prompt optimization. By providing an initial prompt, a dataset, and custom evaluators, Promptim uses an iterative process to refine the prompt and achieve superior results. This framework allows users to define what constitutes "good" or "bad" output using evaluators and even incorporate human feedback via an annotation queue, creating a human-in-the-loop optimization process. The library handles the complexities of the optimization loop, including baseline metric calculation, metaprompt suggestions, and prompt iteration, giving users an intuitive and effective way to improve their AI system's output quality.

Promptim is especially valuable for AI developers and researchers looking for a systematic and reliable method to improve the prompts used with large language models. The capability of the system to incorporate custom evaluators, human-in-the-loop feedback, and metaprompts provide a flexible and effective way to optimize prompt performance to any task you can describe and evaluate. The framework is ideal for those seeking a deeper level of control over their AI output by moving beyond simple trial and error to a more controlled and analytic approach.