The ML community is rapidly exploring techniques for prompting language models (LMs)  and for stacking them into pipelines that solve complex tasks.
Unfortunately, existing LM pipelines
are typically implemented using hard-coded “prompt templates”, i.e. lengthy strings
discovered via trial and error. Toward a more systematic approach for developing and optimizing LM pipelines, we introduce DSPy, a programming model that abstracts LM pipelines astext transformation graphs, i.e. imperative computation graphs where LMs are invoked throughdeclarativemodules. DSPy modules areparameterized, meaning they can learn (by creating and collecting demonstrations)  how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. We design a compiler that will optimize any DSPy pipeline to maximize a given metric. We conduct two case studies, showing that succinct DSPy programs can express and optimize sophisticated LM pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. Within minutes of compiling, a few lines of DSPy allowGPT-3.5andllama2-13b-chatto self-bootstrap pipelines that outperform standard few-shot prompting (generally by over 25% and 65%, respectively)  and pipelines with expert-created demonstrations (by up to 5–46% and 16–40%, respectively) . On top of that, DSPy programs compiled to open and relatively small LMs like 770M-parameterT5andllama2-13b-chatare competitive with approaches that rely on expert-written prompt chains for proprietaryGPT-3.5. DSPy is available athttps://github.com/stanfordnlp/dspy.