dspy-evaluation-patterns

To evaluate DSPy pipelines for optimization, you structurally design metrics and test cases that provide fine-grained feedback, ensuring your optimizer improves module performance consistently. This approach separates inputs from outputs for robust assessment.

When optimizing DSPy modules, you should use metrics designed for fine-grained feedback, such as LLM-as-judge and multi-dimensional scoring. These metrics provide the relevant evaluation feedback necessary for the optimizer to improve effectively.

Building a test case dataset for DSPy evaluation involves constructing diverse examples with sufficient variety, including edge cases. This comprehensive evaluation dataset ensures your DSPy optimization harness validates performance improvements accurately.

Yes, you need prior knowledge of DSPy's module structure and optimization workflows. Structured evaluation patterns apply to advanced DSPy users engaged in iterative optimization, requiring familiarity with program architecture.

Your DSPy optimizer may not be improving because the evaluation harness lacks diverse datasets or accurate metrics. Structured evaluation fixes this by applying fine-grained feedback and input-output separation to guide optimization.