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Computational layers that automate the refinement of model parameters and prompts.
Distinguishing note: Focuses on the engine component of optimization rather than the process itself.
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DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-
Provides a computational layer that iteratively refines instructions and demonstrations based on feedback.