mshumer/gpt-prompt-engineer - GitHub
The GitHub repository "mshumer/gpt-prompt-engineer" is designed as a tool to optimize and streamline the process of prompt engineering for AI models. By effectively utilizing GPT-4 and GPT-3.5-Turbo, it aids users in generating a variety of prompts based on defined use-cases and testing their performance. The system ranks prompts using an ELO rating system, allowing users to identify the most effective ones for their needs. This tool is a boon for developers and researchers who are looking to enhance interaction with AI language models and can be beneficial for tasks across various domains, including content creation, data analysis, and innovation in AI-powered applications.
KEY FEATURES
Prompt Generation: Leverages GPT-4 and GPT-3.5-Turbo to create potential prompts.
Prompt Testing: Evaluates prompt efficacy by testing against set cases and analyzing performance.
ELO Rating System: Ranks prompts based on competitive performance to determine effectiveness.
Classification Version: Specialized for classification tasks matching outputs with expected results.
Portkey & Weights & Biases Integration: Offers optional logging tools for detailed prompt performance tracking.
FAQ
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What is the primary purpose of gpt-prompt-engineer?The primary purpose of gpt-prompt-engineer is to generate, test, and rank prompts to find the most effective ones for interacting with AI language models.
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Which AI models does gpt-prompt-engineer use for prompt generation?It uses GPT-4 and GPT-3.5-Turbo to generate a range of potential prompts for various use-cases.
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How does gpt-prompt-engineer determine the effectiveness of a prompt?The tool uses an ELO rating system to rank prompts based on their performance in generating responses to test cases.
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Is there a version of gpt-prompt-engineer designed for classification tasks?Yes, there is a Classification Version of the tool which evaluates the correctness of prompts for classification tasks.
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How can users contribute to the development of gpt-prompt-engineer?Users can contribute by creating diverse system prompt generators, automating test case generation, and expanding classification support.
