DSPy Optimizes Language Model Pipelines For Self-Improving Programs

DSPy, introduced by Stanford NLP, is a framework that treats language-model prompts as programmable, optimizable pipelines rather than fixed templates. It provides declarative text-transformation graphs with signatures, modules, and optimizers to refine prompts, weights, and multi-step flows for models like GPT-3.5, GPT-4, T5-base, and Llama2-13b. The framework includes tooling and installation instructions to reduce manual prompt engineering and improve smaller open models' competitiveness.