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dspy

Build DSPy programs with declarative signatures and automatic prompt optimization.

Updated Jan 30, 2026
Search Tags:#prompt engineering#rag#agents#ai systems#declarative programming#dspy
One-click install
npx skills add https://github.com/gagan114662/content_books --skill dspy-gagan114662
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/gagan114662/content_books/tree/main/AI-research-SKILLs/16-prompt-engineering/dspy
Command: npx skills add https://github.com/gagan114662/content_books --skill dspy-gagan114662

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the development and optimization of complex AI systems by providing a declarative programming framework for language models, automating prompt engineering and enabling modular AI pipelines.

Core Features & Use Cases

  • Declarative LM Programming: Define AI tasks using signatures (input/output specifications) instead of manual prompt crafting.
  • Automatic Prompt Optimization: Improve model performance systematically using data-driven optimizers.
  • Modular AI Pipelines: Build reusable components (modules) for complex workflows like RAG and agents.
  • Use Case: Develop a question-answering system that automatically refines its prompts based on example data to achieve higher accuracy, or build an agent that can use tools to research information.

Quick Start

Use the dspy skill to predict the answer to the question "What is the capital of France?".

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I automate prompt engineering for language models?▼

Automate prompt engineering by using DSPy's data-driven optimizers to systematically refine and improve language model performance. This shifts manual prompt crafting to automated, declarative programming.

What is declarative programming for AI systems?▼

Declarative programming for AI systems means defining tasks via input and output signatures instead of manual prompts. DSPy enables this approach to streamline complex language model workflows.

How do I build a modular RAG system with Python?▼

Build a modular RAG system by defining reusable Pythonic components and signatures. DSPy facilitates creating these modular AI pipelines for complex workflows like retrieval-augmented generation.

Can I use DSPy with OpenAI and Anthropic models?▼

Yes, DSPy integrates with OpenAI and Anthropic models. You can configure these language model providers to execute and optimize your declarative AI pipelines and agents.

How do I develop agents that use tools to research information?▼

Develop research agents by defining modular Pythonic components that orchestrate tool usage. DSPy enables building agents capable of researching information and executing complex workflows.

Why should I use DSPy instead of manual prompt crafting?▼

Use DSPy to eliminate manual prompt crafting by defining task signatures and applying automatic prompt optimization. This systematic approach yields higher accuracy and reusable modular pipelines.

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