dspy-evaluation-patterns

What problem does it solve?

This Skill helps ensure the effectiveness of DSPy optimization by guiding structured evaluation of DSPy programs, focusing on accurate metric design and harness configuration for robust optimization.

Core Features & Use Cases

• Structured Evaluation Approach: Facilitates the creation of comprehensive evaluation datasets and the design of appropriate metrics for DSPy modules.
• Example Dataset Construction: Offers guidelines for constructing diverse datasets, including handling edge cases and ensuring sufficient variety.
• Metric Design: Guides the selection and creation of relevant metrics for optimization feedback, such as LLM-as-judge and multi-dimensional scoring.
• Use Case: For a DSPy optimization program, this Skill provides the framework for constructing a test case and evaluation harness that ensures the optimizer improves effectively and consistently.

Quick Start

Use the dspy-evaluation-patterns skill to design a test case for a DSPy module. Provide examples, design an evaluation harness, and run an optimization to validate performance improvements.