prompt-engineering

You can optimize prompts by initializing a DSPy workflow with your prompt and an evaluation dataset to systematically rewrite and analyze inputs, which improves AI response accuracy and consistency.

DSPy prompt engineering is an end-to-end optimization process that applies specialized modules for grading, rewriting, and analyzing prompts using defined metrics and data structures to orchestrate higher quality outputs.

Yes, you can compare prompt variants using the ComparatorModule, which evaluates different prompt configurations against defined metrics to determine which variant yields higher accuracy and reliability.

Yes, an evaluation dataset is required to initialize the DSPy optimization workflow, providing the baseline metrics needed to measure accuracy improvements and guide the automated prompt rewriting process.

Use the EvidenceModule and GraderModule within a DSPy workflow to apply structured evaluation metrics, ensuring your evidence extraction prompts reliably yield accurate and relevant outputs.

Optimization may fail if the evaluation dataset lacks sufficient coverage or if the defined metrics do not align with the target task, causing the rewriting modules to optimize for the wrong output patterns.