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GPT Prompt Engineer

GPT Prompt Engineer

Automated prompt engineering
在 GitHub 查看 ↗直链 · 无中转页
官方说明Automated prompt engineering. It generates, tests, and ranks prompts to find the best ones.
来源明细
收录自 steven2358/awesome-generative-ai 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-14 · 最近见到 2026-10-16 · 共见到 4 次 · 被 3 个来源列出
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综合分 303 · 3 个来源 · mcp
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怎么安装?
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我们收录于 2026-10-14;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

gpt-prompt-engineer

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

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Overview
Prompt engineering is kind of like alchemy. There's no clear way to predict what will work best. It's all about experimenting until you find the right prompt. gpt-prompt-engineer is a tool that takes this experimentation to a whole new level.
**Simply input a description of your task and some test cases, and the system will generate, test, and rank a multitude of prompts to find the ones that perform the best.**
*New 3/20/24: The Claude 3 Opus Version*
I've added a new version of gpt-prompt-engineer that takes full advantage of Anthropic's Claude 3 Opus model. This version auto-generates test cases and allows for the user to define multiple input variables, making it even more powerful and flexible. Try it out with the claude-prompt-engineer.ipynb notebook in the repo!
*New 3/20/24: Claude 3 Opus -> Haiku Conversion Version*
This notebook enables you to build lightning-fast, performant AI systems at a fraction of the typical cost. By using Claude 3 Opus to establish the latent space and Claude 3 Haiku for the actual generation, you can achieve amazing results. The process works by leveraging Opus to produce a collection of top-notch examples, which are then used to guide Haiku in generating output of comparable quality while dramatically reducing both latency and cost per generation. Try it out with the opus-to-haiku-conversion.ipynb notebook in the repo!
Features
- **Prompt Generation**: Using GPT-4, GPT-3.5-Turbo, or Claude 3 Opus, gpt-prompt-engineer can generate a variety of possible prompts based on a provided use-case and test cases.
- **Prompt Testing**: The real magic happens after the generation. The system tests each prompt against all the test cases, comparing their performance and ranking them using an ELO rating system.
- **ELO Rating System**: Each prompt starts with an ELO rating of 1200. As they compete against each other in generating responses to the test cases, their ELO ratings change based on their performance. This way, you can easily see which prompts are the most effective.
- **Classification Version**: The gpt-prompt-engineer -- Classification Version notebook is designed to handle classification tasks. It evaluates the correctness of a test case by matching it to the expected output ('true' or 'false') and provides a table with scores for each prompt.
- **Claude 3 Version**: The claude-prompt-engineer notebook is designed to work with Anthropic's Claude 3 Opus model. It auto-generates test cases and allows for multiple input variables, making it even more powerful and flexible.
- **Claude 3 Opus -> Haiku Conversion Version**: Designed to preserve Opus' quality for your use-case while getting the speed + cost benefits of using Haiku.
- **Weights & Biases Logging**: Optional logging to Weights & Biases of your configs such as temperature and max tokens, the system and user prompts for each part, the test cases used and the final ranked ELO rating for each candidate prompt. Set use_wandb to True to use.
- **Portkey**: Optional tool to log and trace all the prompt chains and their responses. Set use_portkey to True to use.
来源
awesome · https://github.com/mshumer/gpt-prompt-engineer
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