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Methods for providing semantic context to model inputs and outputs through descriptive naming.
Distinguishing note: Focuses on semantic naming for model interpretation rather than general variable naming.
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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-
Assigns descriptive names to fields to provide semantic context for model interpretation.