DSPy Declarative AI Programming

About

This skill integrates DSPy, the groundbreaking Stanford NLP framework, into your environment for systematic language model programming. It shifts focus from manual, fragile prompt engineering to defining clear program logic through Signatures and Modules, using data-driven optimizers to refine model behavior automatically. Whether you are building multi-stage AI pipelines, production-grade RAG systems, or self-correcting agents, this skill provides the implementation patterns and best practices needed to create reliable, portable, and high-performance AI applications across diverse model providers.

Key Features

• Advanced RAG system construction with retrieval, reranking, and self-consistency
• Automatic prompt optimization using data-driven optimizers like MIPRO and BootstrapFewShot
• 384 GitHub stars
• Modular patterns for Chain of Thought, ReAct, and Program of Thought reasoning
• Declarative LLM programming via reusable Signatures and Modules
• Multi-provider support including Anthropic Claude, OpenAI, and local Ollama models

Use Cases

• Developing complex multi-stage agentic workflows that self-improve based on training data
• Building production-grade RAG systems with automated retrieval and generation tuning
• Systematically improving model accuracy through custom evaluation metrics and assertion-driven optimization