GPT Prompt Engineer
The 'gpt-prompt-engineer' is an AI tool available on GitHub, created by 'mshumer'. The tool aims to enhance the workload of data engineers working with Generati...
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What is GPT Prompt Engineer?
GPT Prompt Engineer is an open-source automated prompt engineering tool that eliminates the guesswork from creating optimal prompts for AI language models. Instead of manually experimenting with different prompts, users simply input a description of their task and some test cases, and the system automatically generates, tests, and ranks multiple prompts to identify the ones that perform best. The tool takes prompt experimentation to a new level by running prompts through a ranked tournament system.
GPT Prompt Engineer pricing
Pricing model: Free
The tool itself is completely free as it is open-source under the MIT license. However, users must pay for their own API usage with OpenAI (for GPT-4 or GPT-3.5-Turbo) or Anthropic (for Claude 3 Opus/Haiku). There is no free tier, subscription plan, or paid version of the tool itself - you only pay for the API tokens consumed when running the notebook. The repository explicitly warns that generating many prompts can get expensive due to API costs, recommending 10 as a good starting point for number of prompts to generate.
GPT Prompt Engineer pros
- Fully open-source with MIT license for free use and modification
- Automates the entire prompt engineering workflow from generation to ranking
- Uses an ELO rating system for competitive prompt ranking starting at 1200 ELO
- Supports multiple AI models including GPT-4, GPT-3.5-Turbo, and Claude 3 Opus
- Includes specialized Classification Version for binary true/false classification tasks
- Claude 3 version auto-generates test cases based on use-case description
- Claude 3 version supports multiple input variables for complex prompts
- Claude 3 Opus to Haiku conversion preserves quality while reducing cost and latency
- Optional Weights & Biases logging tracks temperature, tokens, prompts, and ELO ratings
- Optional Portkey integration logs and traces all prompt chains and responses
- Works in Google Colab or local Jupyter notebooks for flexible deployment
- Simple API with single function call generate_optimal_prompt for easy usage
- 9.6k GitHub stars indicate strong community trust and adoption
- Active contributor base with 7 contributors enabling ongoing development
- Comprehensive test case evaluation compares all prompts against all cases
- Final results displayed in sorted table with descending ELO ratings
- Covers diverse use cases from landing page headlines to email responses
GPT Prompt Engineer cons
- Requires paid OpenAI or Anthropic API key to function
- Can get expensive when generating many prompts due to API costs
- Only supports Jupyter Notebook format (100% Jupyter Notebook language)
- No official releases published yet on GitHub
- No packages published for easy installation via pip or npm
- User must manually define test cases for GPT-4 version
- Limited to binary classification (true/false) in Classification Version
- No graphical user interface, only code-based notebook interaction
Frequently asked questions about GPT Prompt Engineer
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. It automates the prompt experimentation process by taking a task description and test cases, then running multiple AI-generated prompts through a tournament system to identify the best performer.
Which AI models does gpt-prompt-engineer use for prompt generation?
The tool uses GPT-4 and GPT-3.5-Turbo for the main GPT version, and Anthropic's Claude 3 Opus for the Claude 3 version. The Claude 3 Opus to Haiku conversion version uses Claude 3 Opus to establish quality examples and Claude 3 Haiku for fast, cost-effective generation.
How does gpt-prompt-engineer determine the effectiveness of a prompt?
The tool uses an ELO rating system where each prompt starts with an ELO rating of 1200. As prompts compete against each other in generating responses to test cases, their ELO ratings change based on performance. The final ELO ratings are displayed in a table sorted in descending order, with higher ratings indicating better prompts.
Is there a version of gpt-prompt-engineer designed for classification tasks?
Yes, there is a Classification Version called 'gpt-prompt-engineer -- Classification Version' that is specifically designed for classification tasks. It evaluates the correctness of test cases by matching outputs to expected results ('true' or 'false') and provides a table with scores for each prompt.
How can users contribute to the development of gpt-prompt-engineer?
Users can contribute by creating diverse system prompt generators that create different styles of prompts (examples, verbose, short, markdown, etc.), automatically generating test cases, and expanding the classification version to support more than two classes using tiktoken. The repository explicitly states 'Contributions are welcome!'
What is the Claude 3 Opus to Haiku Conversion Version and when should I use it?
This version enables building lightning-fast, performant AI systems at a fraction of typical cost. It uses Claude 3 Opus to produce top-notch examples that establish the latent space, then uses Claude 3 Haiku for actual generation, achieving comparable quality while dramatically reducing latency and cost per generation. Use it when you need speed and cost efficiency while preserving Opus-quality results.
What format should test cases be in for the different versions?
For the GPT-4 version, test cases are objects with a 'prompt' field containing the input to test. For the Classification Version, test cases include both 'prompt' and 'output' fields with expected 'true' or 'false' values. For the Claude 3 version, you define input_variables with variable names and descriptions, and test cases are auto-generated based on the use-case and variables.
What are Weights & Biases and Portkey integrations used for?
Weights & Biases logging is optional and tracks your configs (temperature, max tokens), system and user prompts for each part, test cases used, and final ranked ELO ratings for each candidate prompt. Set 'use_wandb' to True to enable. Portkey is optional and logs and traces all prompt chains and their responses. Set 'use_portkey' to True to enable.
How many prompts should I generate and what affects the cost?
The repository recommends 10 as a good starting point for the number of prompts to generate. Cost is affected by the number of prompts generated, as each prompt requires API calls for generation, testing against all test cases, and ranking. The warning explicitly states 'this can get expensive if you generate many prompts' due to API token consumption with OpenAI or Anthropic.