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dspy

Build DSPy programs with signatures, modules, and automatic prompt optimization.

1|Updated Feb 21, 2026
Search Tags:#prompt engineering#optimization#rag#agents#ai systems#declarative programming#dspy
One-click install
npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill dspy-tianhao909
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/16-prompt-engineering/dspy
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill dspy-tianhao909

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dspy, openai, anthropic, and includes references (resource) components.

What problem does it solve?

This Skill automates the complex and time-consuming process of prompt engineering and AI system development, enabling users to build sophisticated AI applications more efficiently.

Core Features & Use Cases

  • Declarative Programming: Define AI system logic using signatures and modules, rather than manual prompt crafting.
  • Automatic Prompt Optimization: DSPy's optimizers systematically improve prompts based on data and metrics.
  • Modular AI Pipelines: Build reusable and composable AI components for complex workflows like RAG and agents.
  • Use Case: Develop a question-answering system that not only retrieves information but also reasons through it, optimizes its own prompts for better accuracy, and can be easily updated or extended.

Quick Start

Use the dspy skill to build a question-answering system by defining a signature and using a Predict module.

Frequently Asked Questions about dspy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize prompts automatically instead of manual prompt engineering?▼

Automatic prompt optimization uses DSPy optimizers to systematically improve language model prompts based on data and metrics, replacing manual prompt crafting with declarative programming.

What is declarative programming for building complex AI systems?▼

Declarative programming for AI systems defines logic using signatures and modules rather than manual prompts, enabling modular RAG pipelines and agents through systematic LM programming.

Can I build modular RAG systems and agents using DSPy?▼

Yes, you can build modular RAG systems and agents using DSPy by defining composable AI components and signatures, facilitating reusable pipelines for complex reasoning workflows.

Does DSPy work with OpenAI and Anthropic language models?▼

Yes, DSPy works with OpenAI and Anthropic models, requiring these frameworks to define signatures, modules, and optimizers for automatic prompt optimization and systematic LM programming.

How do I create a question-answering system that reasons through retrieved information?▼

Create a question-answering system by defining a DSPy signature and using a Predict module to reason through retrieved information, optimizing prompts automatically for better accuracy.

What are the limitations of using DSPy for AI system development?▼

Using DSPy requires the DSPy framework for defining signatures, modules, and optimizers, meaning users must understand declarative programming concepts to build complex AI systems effectively.

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