dspy-evaluation

What problem does it solve?

This Skill helps you design and run evaluations for DSPy optimization, ensuring that your model's performance is accurately measured against a well-constructed test case and a relevant metric.

Core Features & Use Cases

• Example Construction: Learn how to create diverse and representative datasets using dspy.Example for optimal model testing.
• Metric Design: Understand the different metric types (bool, float, Prediction) and their uses in optimizing models with DSPy.
• Evaluation Harness: Run comprehensive evaluations with the Evaluate harness, tracking baseline and optimized performance.
• Use Case: Before deploying a new DSPy model, use this Skill to evaluate its performance against a set of benchmarks and iterate on metrics to ensure the model is functioning as intended.

Quick Start

To start using the dspy-evaluation Skill, first construct a test set using dspy.Example and define your evaluation metric. Then, run the Evaluate harness to compare your model's performance before and after optimization.