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DSPy – Programming–not prompting–LMs
The framework for programming—rather than prompting—language models.
DSPy provides you with the tools to compile high-level code with natural language annotations into the low-level computations, prompts, or weight updates that align your LM with your program’s structure and metrics. Given a retrieval index to search, your favorite dspy.LM, and a small trainset of questions and ground-truth responses, the following code snippet can optimize your RAG system with long outputs against the built-in SemanticF1 metric, which is implemented as a DSPy module. Compared to monolithic LMs, DSPy's modular paradigm enables a large community to improve the compositional architectures, inference-time strategies, and optimizers for LM programs in an open, distributed way.
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