gepa-demo

Optimize LLM prompts with the GEPA algorithm and web dashboard.

58|5|Updated Dec 21, 2025

Search Tags:#prompt engineering #ai development #data science #llm optimization #dspy #gepa

Ready to run

npx skills add https://github.com/raveeshbhalla/dspy-gepa-logger --skill gepa-demo

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

Prompt Optimization: Leverages the GEPA algorithm to iteratively improve LLM prompts based on your data and evaluation criteria.
Observability: Provides real-time visualization of the optimization process through a web dashboard, allowing you to track prompt evolution and performance.
Use Case: A marketing team wants to generate better ad copy. They can use this Skill to optimize their prompt, feeding it examples of good and bad ad copy, and the Skill will refine the prompt to produce higher-quality results.

Quick Start

Use the gepa-demo skill to start optimizing your LLM prompts by following the interactive guide.

Frequently Asked Questions about gepa-demo

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize LLM prompts using my own dataset?▼

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.

What is the GEPA algorithm for prompt engineering?▼

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.

Do I need Python and Node.js to run prompt optimization locally?▼

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.

Can I track LLM prompt performance during the optimization process?▼

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.

What's the best way to refine prompts for specific tasks like ad copy generation?▼

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.

When should I use automated prompt optimization over manual refinement?▼

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.