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dspy

Automate AI system construction with declarative language model programming and data-driven prompt optimization.

Updated May 25, 2026
Search Tags:#prompt optimization#information retrieval#declarative programming#AI system development#language model programming
One-click install
npx skills add https://github.com/zaiyemeiyou404/Hermes --skill dspy-zaiyemeiyou404
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/zaiyemeiyou404/Hermes/tree/main/backup/skills/mlops/research/dspy
Command: npx skills add https://github.com/zaiyemeiyou404/Hermes --skill dspy-zaiyemeiyou404

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill empowers users to build complex AI systems with declarative language model programming and automatic prompt optimization, automating tasks and improving outcomes.

Core Features & Use Cases

  • Declarative Language Model Programming: Build modular AI pipelines with declarative signatures, optimizing for maintainability and portability.
  • Automatic Prompt Optimization: Use data-driven methods to optimize prompts and improve model outputs.
  • Use Case: Create a multi-hop RAG system for information retrieval and reasoning, or an agent system for research and calculations.

Quick Start

Use the dspy skill to generate a reasoning path for the math problem "If John has 5 apples and gives 2 to Mary, how many does he have?".

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I build a multi-hop RAG system with declarative programming?▼

You build a multi-hop RAG system by defining modular pipelines with declarative signatures, allowing automatic prompt optimization to handle information retrieval and reasoning tasks.

Can I use OpenAI and Anthropic models for automatic prompt optimization?▼

Yes, automatic prompt optimization integrates directly with OpenAI and Anthropic models, using data-driven methods to iteratively refine prompts and improve language model outputs.

What is declarative language model programming and when do I need it?▼

Declarative language model programming abstracts complex AI system logic into modular signatures, needed when building maintainable and portable reasoning pipelines or agent systems.

Does this approach work for building agent systems that perform research and calculations?▼

Yes, you can construct agent systems for research and calculations by defining declarative AI pipelines that leverage language models for automated reasoning and data processing.

How do I optimize prompts for complex AI system development without manual tuning?▼

You optimize prompts automatically by applying data-driven optimization techniques to your declarative language model programs, eliminating manual tuning for complex AI pipelines.

What are the limitations of using declarative signatures for information retrieval tasks?▼

Declarative signatures require dependencies on dspy, openai, and anthropic libraries, meaning complex information retrieval tasks are constrained by underlying language model capabilities and API availability.

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