Evaluating and Optimizing LLM Applications with DSPy

This is the DSPy link I would hand to someone who keeps saying "prompt engineering" when they really mean eval-driven optimization. The example is concrete, costed, and honest about train, validation, and holdout splits.

Especially useful because it makes LLM application work feel closer to data science: define a task, build examples, pick a metric, optimize, and check whether the gains survive outside the optimizer.