Systematic LLM Prompt Engineering Using DSPy Optimization

A systematic methodology for LLM prompt engineering is presented using the DSPy optimization library. It addresses the challenges of manual prompt iteration by introducing an "LLM judge" to automatically evaluate the quality of a generator model's output. The process involves creating a gold-standard dataset to train the judge, which is then used to systematically optimize the generator's prompt. This entire workflow is demonstrated through a practical example of generating helpful customer service responses, highlighting how to automate and version prompt development.