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Modules that execute multiple rollouts to select the best output based on reward functions.
Distinguishing note: Focuses on ensemble-based selection, distinct from single-pass generation.
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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-
Runs modules multiple times with different rollouts to select the best output based on reward functions.