dspy

Automate prompt optimization by using DSPy's data-driven optimizers to systematically improve large language model performance. This declarative framework replaces manual prompt engineering with programmatic signature definitions and modular pipelines.

Declarative AI programming abstracts manual prompt engineering by defining tasks using signatures and modules. It allows developers to build complex pipelines by specifying what the AI should do rather than manually crafting instructions for the model.

Build a retrieval augmented generation pipeline by utilizing DSPy's modular components to retrieve relevant information and generate context-aware responses. Define signatures for your tasks and assemble reusable modules to structure the RAG system.

Yes, integrating with large language model providers like OpenAI or Anthropic is required to use this declarative AI framework. You must connect a provider to supply the underlying language model capabilities for the pipelines.

Yes, DSPy supports building modular AI agents by providing reusable components for agentic systems. You can assemble these modules to create agents that understand queries, retrieve information, and generate accurate context-aware responses.