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Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs.
Features
- Allows the creation of modular AI systems using Python code
- Offers tools to refine prompts and weights for improved model performance
- Suitable for building classifiers, retrieval-augmented generation (RAG) pipelines, and agent loops
- Enables fast development cycles for AI system components
- Supported by comprehensive documentation and an active user community
- Designed to work seamlessly with various language models
Project Samples
License
MIT LicenseFollow DSPy
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