DSPy provides a systematic approach to LLM prompt optimization by abstracting prompts into modular Python code and offering automated optimization tools. The article demonstrates building an LLM judge system for customer service responses, using MIPROv2 optimizer to improve both judge and generator prompts. Key components include creating gold standard datasets, implementing evaluation metrics, and using automated optimization to achieve measurable improvements in prompt performance while maintaining reproducibility and preventing overfitting.
Table of contents
1.0 The challenge of prompt iteration2.0 Who evaluates the output?3.0 Adding complexity4.0 Purpose of this article5.0 Dataset and Objective6.0 Baseline generator and judge development set7.0 The judge training dataset8.0 Optimizing the judge prompt9.0 Using the optimized judge to optimize the generator10.0 Important learningsSort: