Programmatic Prompt Optimization: Building a Spam Filter with DSPy and MIPROv2
Most prompt engineering today is manual and intuition-driven. We tweak wording, rearrange instructions, and evaluate outputs by gut feel. But as LLM applications move from prototypes to production, this approach doesn't scale. What if we could replace intuition with data, optimizing prompts programmatically against measurable metrics similar to how we optimize model weights?