dspy-metrics-and-feedback
CommunityOptimize DSPy metrics and design feedback for GEPA's reflection loop
Authorhung-phan
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides metric design patterns for DSPy optimization and feedback mechanisms for GEPA's reflection loop, enhancing the effectiveness of the optimization process.
Core Features & Use Cases
- Metric Design: Offers various metric designs like F1, semantic similarity, and LLM-as-judge for DSPy optimization.
- Feedback for GEPA: Guides in designing feedback for GEPA's reflection loop, improving the reflective learning process.
- Use Case: When developing or fine-tuning a DSPy model and need to ensure that the metrics used are effective and provide meaningful feedback for model improvement.
Quick Start
Generate a metric for a given prediction using the provided example.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: dspy-metrics-and-feedback Download link: https://github.com/hung-phan/ml-skills/archive/main.zip#dspy-metrics-and-feedback Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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