This episode explores DSPy, a declarative framework that enables developers to build modular software by treating Large Language Models as first-class citizens within a proper Python program. We discuss how core primitives like signatures and modules allow for the decomposition of logic into composable systems that remain robust despite model or paradigm shifts. Finally, we dive into the power of DSPy optimizers, which iteratively refine prompts and metrics to improve performance, often rivaling or exceeding traditional fine-tuning methods.