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dspy

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

Updated Aug 27, 2026
Search Tags:#prompt optimization#rag#agents#ai systems#declarative programming#dspy
One-click install
npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill dspy-axgz21
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/AXGZ21/hermes-agent-railway/tree/main/skills/mlops/dspy
Command: npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill dspy-axgz21

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the creation of complex AI systems by enabling declarative programming, automatic prompt optimization, and modular RAG systems and agents.

Core Features & Use Cases

  • Declarative LM Programming: Define AI tasks using signatures and modules, not just raw prompts.
  • Automatic Prompt Optimization: Improve model outputs systematically using data-driven methods.
  • Modular RAG & Agents: Build maintainable and portable AI pipelines.
  • Use Case: Develop a sophisticated question-answering system that can retrieve information, reason over it, and provide accurate answers, all while continuously improving its performance through automated optimization.

Quick Start

Use the dspy skill to build a basic question answering system by defining a signature and 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 build AI systems with declarative programming instead of manual prompts?▼

Declarative programming for AI systems lets you define tasks using structured signatures and modules rather than raw prompts. This approach enables automatic prompt optimization and systematic language model programming.

What is automatic prompt optimization and how does it improve model outputs?▼

Automatic prompt optimization improves language model outputs systematically using data-driven methods. Instead of manual tweaking, it refines prompts automatically to enhance reasoning and accuracy across complex pipelines.

How do I create a modular RAG system for question answering?▼

You can build a modular RAG system by defining a signature and a predict module. This creates a maintainable pipeline that retrieves information, reasons over it, and provides accurate answers.

Can I use OpenAI and Anthropic models with declarative AI programming frameworks?▼

Yes, declarative AI programming frameworks support OpenAI and Anthropic dependencies. You can integrate these language models into your structured signatures to build and optimize complex agents.

What is the best way to structure complex AI agents and pipelines?▼

The best way to structure complex AI agents is using modular components defined by declarative signatures. This ensures pipelines remain maintainable and portable while continuously improving through automated optimization.

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