https://arxiv.org/abs/2310.03714
[2310.03714] DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
Abstract page for arXiv paper 2310.03714: DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
dspycompilinglanguagemodelcalls
https://dspy.ai/tutorials/rag/
Retrieval-Augmented Generation (RAG) - DSPy
The framework for programming—rather than prompting—language models.
retrievalaugmentedgenerationragdspy
https://dropbox.tech/machine-learning/optimizing-dropbox-dash-relevance-judge-with-dspy
How we optimized Dash's relevance judge with DSPy - Dropbox
We used DSPy to turn prompt engineering for our relevance judge into a measurable, automated optimization loop, improving task performance, cost, and how...
optimizeddashrelevancejudgedspy
https://dspy.ai/community/how-to-contribute/
Contributing - DSPy
The framework for programming—rather than prompting—language models.
contributingdspy
https://arxiv.org/abs/2312.13382
[2312.13382] DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
Abstract page for arXiv paper 2312.13382: DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
dspyassertionscomputationalconstraintsself
https://dspy.ai/
DSPy
The framework for programming—rather than prompting—language models.
dspy
https://github.com/stanfordnlp/dspy
GitHub - stanfordnlp/dspy: DSPy: The framework for programming—not prompting—language models ·...
DSPy: The framework for programming—not prompting—language models - stanfordnlp/dspy
the frameworkgithubdspymodels
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