This is a tool developed to measure and optimize the performance of the GPT-4 API when solving seq2seq NLP problems, such as text/code generation, translation/stylization, summarization, text correction, named entity recognition, and text classification.
The tool measures the quality of the language learning model (LLM) on various prompts, helping you find the optimal one. It provides mechanisms for prompt optimization and quality measurement with focus on minimal prompt length and cost-effectiveness.
You can find the detailed tutorial here: https://medium.com/@unicornporated/promptgpt-optimizing-the-prompt-for-gpt-4-83042f26a760
- Quality Measurement: Compares model output with expected output from a test dataset and calculates a similarity score.
- Prompt Optimization: Varies the system message and choice of examples to find the optimal prompt.
- Cache Usage: Stores model run results in a cache to save time and money.
- Cost-Benefit Analysis: Helps determine if the quality enhancement of using GPT-4 justifies its higher cost.