gpt-prompt-engineer is a prompt optimization tool that automatically generates, tests, and ranks multiple prompts to identify the highest-performing ones for a given task.
The tool addresses the unpredictability of prompt engineering by automating the experimentation process. Users provide a task description and test cases, and the system generates candidate prompts using GPT-4, GPT-3.5-Turbo, or Claude 3 Opus. Each generated prompt is then tested against all provided test cases, and performance is ranked using an ELO rating system where prompts compete against each other and their ratings adjust based on how well they perform. This transforms prompt engineering from manual trial-and-error into a systematic evaluation framework.
The project offers specialized versions for different use cases. The main version suits general-purpose prompt optimization, while a classification-specific version evaluates correctness by matching outputs to expected true or false values. A Claude 3 Opus version adds the ability to auto-generate test cases and define multiple input variables. Additionally, an Opus-to-Haiku conversion version enables cost and latency reduction by using Opus to establish high-quality examples that guide Haiku in generating comparable output at lower expense. The tool is designed to run in Jupyter notebooks and can be executed directly in Google Colab, making it accessible without local setup.
Development activity shows consistent expansion of capabilities with multiple specialized versions added to address different problem domains. The project maintains active engagement with the community through social channels and newsletter signup mechanisms. The codebase remains focused on notebook-based implementations, suggesting a preference for interactive, exploratory workflows over library-style distribution.
