Optimize LLM prompts with the GEPA algorithm and web dashboard.
npx skills add https://github.com/raveeshbhalla/dspy-gepa-logger --skill gepa-demo
Please help me install this Agent Skill. Skill: gepa-demo Source: https://github.com/raveeshbhalla/dspy-gepa-logger/tree/main/skills/gepa-demo Command: npx skills add https://github.com/raveeshbhalla/dspy-gepa-logger --skill gepa-demo
This Skill automates the complex and time-consuming process of optimizing prompts for Large Language Models (LLMs), leading to more accurate and efficient AI responses.
Use the gepa-demo skill to start optimizing your LLM prompts by following the interactive guide.
High-intent search queries and answers about installing and using this skill.
You can optimize LLM prompts by providing your dataset and defining evaluation metrics. The Skill uses the GEPA algorithm to iteratively refine the prompt based on your specific data and criteria, improving AI response accuracy.
The GEPA algorithm is a method for iteratively improving LLM prompts. It automates prompt refinement by evaluating prompt performance against your custom datasets and defined metrics to generate higher-quality AI outputs.
Yes, you need Python, Node.js, npm, and git installed in your environment. These dependencies are required to execute the iterative optimization process and run the real-time web dashboard visualization.
Yes, you can track prompt evolution through a real-time web dashboard. This observability feature allows you to visualize the iterative optimization process and monitor how prompt performance changes over time.
To refine prompts for tasks like ad copy generation, feed the algorithm examples of good and bad outputs. The Skill iteratively optimizes the prompt based on these examples and your evaluation metrics to consistently produce higher-quality results.
Automated prompt optimization is ideal when manual refinement becomes time-consuming or complex. Use it when you have a dataset and clear evaluation metrics, allowing the algorithm to iteratively improve LLM responses without manual trial and error.
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