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Tools for executing evaluation metrics across datasets concurrently to analyze performance.
Distinguishing note: Focuses on parallel execution of evaluation tasks rather than sequential testing.
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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 metric functions across datasets in parallel to aggregate performance reports.