What problem does it solve?
DSPy removes the need to hand-tune brittle prompts when building multi-step language model applications, making it easier to create reliable AI workflows that improve from data.
Core Features & Use Cases
- Declarative task signatures for structured inputs and outputs.
- Reusable modules for prediction, chain-of-thought reasoning, tool use, and typed extraction.
- Optimizers like BootstrapFewShot, MIPRO, and BootstrapFinetune for systematic prompt improvement.
- Well suited for RAG systems, research agents, classifiers, summarizers, and multi-stage support workflows.
- Example use case: convert a manual research assistant into an optimized agent that retrieves sources, reasons over evidence, and returns validated answers.
Quick Start
Use the dspy skill to turn a question-answering workflow into an optimized multi-stage pipeline with retrieval, reasoning, and tool use.