GPT Prompt Engineer - ai tOOler
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GPT Prompt Engineer
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Prompt engineering (7)

GPT Prompt Engineer

Improve your code with help from AI.

Tool Information

The 'gpt-prompt-engineer' is an AI tool designed to make life easier for data engineers by automating the process of creating effective prompts for GPT models.

Available on GitHub and developed by 'mshumer', this tool is a game-changer for those working with Generative Pretrained Transformer models. One of the biggest challenges in using these models is coming up with prompts that actually work well, which often involves a lot of trial and error. That's where the 'gpt-prompt-engineer' comes in—it takes care of the heavy lifting, allowing you to save time and resources.

The tool comes with a variety of helpful files and notebooks, such as 'Instruct_Prompt > Base_Model_Prompt_Converter.ipynb', 'XL_to_XS_conversion.ipynb', 'claude_prompt_engineer.ipynb', and 'gpt_prompt_engineer.ipynb'. These resources are designed to simplify the prompt generation process, making it smoother and more efficient for users.

Plus, it operates under the MIT License, which means you have the freedom to use, modify, and share the software as you see fit. Since it’s hosted on a public repository, the tool invites contributions from the community, encouraging collaboration and ongoing development. It's a great way to benefit from collective expertise while helping to enhance the tool further.

Pros and Cons

Pros

  • Can define multiple input variables
  • Built-in system for testing and ranking prompts
  • Encourages community contributions
  • Optional logging to Weights & Biases
  • Has many files and notebooks
  • Ability to add Anthropic API key
  • Removes manual work
  • Actively maintained
  • Public GitHub repository
  • Allows changes to software
  • ELO rating system
  • and ranks prompts
  • Automates prompt setup for Claude
  • Resource saving
  • Prompt creation
  • Manages classification tasks
  • Free to use under MIT License
  • Works with Anthropic's Claude 3 Opus
  • Includes 'XL to XS conversion'
  • Improves Opus and Haiku models
  • Creates
  • Optional logging to Portkey
  • Saves time and resources
  • tests
  • Works with Jupyter Notebook
  • Manages Claude 3 Opus and Haiku conversion
  • Option to use Google Colab
  • Accepts real-time information
  • Helps create prompts automatically

Cons

  • Needs API key setup
  • Manual test case setup
  • Optional logging to other platforms
  • May have delays with large datasets
  • Only works with Jupyter notebooks
  • Possible high running costs

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