Can I use different models for the task and the judge?

Absolutely. It is recommended to use a smaller, cheaper model for the main task and a larger, more capable model (like Claude 3.5 Sonnet) as the Judge or Reflective LM.

What is the GEPA algorithm in prompt optimization?

GEPA (Genetic Pareto) is an algorithm that uses textual feedback and Pareto frontiers to select prompts that perform reliably across a wide variety of test cases rather than just maximizing an average score.

How does the observability dashboard work?

The skill sets up a local web server (Next.js/Prisma) that visualizes the optimization runs, allowing you to track scores and log traces in real-time through your browser.

Do I need an existing dataset to use this skill?

Yes, you need a dataset in CSV, JSON, or JSONL format. The skill will help you inspect your data and map input/output fields for the optimization process.

GEPA Prompt Optimizer & DSPy Guide

Name: GEPA Prompt Optimizer & DSPy Guide
Author: raveeshbhalla

byraveeshbhalla

•

Ciencia de Datos y ML

Optimizes LLM prompts using the GEPA algorithm and DSPy framework with integrated observability and Pareto-based performance tuning.

This skill automates the complex process of prompt engineering by implementing the GEPA (Genetic Pareto) algorithm through the DSPy framework. It guides users through a structured workflow: from dataset inspection and custom grader creation to iterative optimization. Unlike simple reward-based optimizers, this tool leverages textual feedback and Pareto frontiers to ensure prompts are not only high-scoring but consistently reliable across diverse edge cases. It includes a built-in web dashboard to provide real-time visibility into the optimization process, making it an essential tool for developers moving from manual prompt hacking to systematic AI engineering.

Características Principales

01Support for multi-model configurations using task, reflective, and judge LLMs

02GEPA algorithm implementation using Pareto frontiers for reliable performance

03Interactive dataset mapping and intelligent grader generation

04Real-time observability via an integrated web-based dashboard

05Automated DSPy workflow for systematic prompt optimization

0645 GitHub stars

Casos de Uso

01Transitioning from manual prompt engineering to a data-driven optimization loop

02Visualizing the performance impact of different prompt versions across a dataset

03Refining production-grade prompts for high-accuracy applications

Características Principales

01Support for multi-model configurations using task, reflective, and judge LLMs

02GEPA algorithm implementation using Pareto frontiers for reliable performance

03Interactive dataset mapping and intelligent grader generation

04Real-time observability via an integrated web-based dashboard

05Automated DSPy workflow for systematic prompt optimization

0645 GitHub stars

Casos de Uso

01Transitioning from manual prompt engineering to a data-driven optimization loop

02Visualizing the performance impact of different prompt versions across a dataset

03Refining production-grade prompts for high-accuracy applications