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Static, over-engineered prompts quickly lose effectiveness as models and data change. DSPy replaces inflexible text-based prompts with dynamic contract-based Python code, so your prompts can freely adapt and scale. In Building LLM Applications with DSPy, AI engineers Serj Smorodinsky and Brett Kennedy introduce the powerful DSPy framework and show you how it can revolutionize the way you think of prompt and context engineering. In this practical guide, you’ll learn how DSPy automatically optimizes context, evaluates prompt effectiveness, and tweaks your prompts as models drift and change. As you go, you’ll get tips and techniques to maintain stable inference results as your apps and agents evolve.