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JimmyChen-NXP
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dspy-ruby

Create type-safe LLM applications in Ruby with DSPy.rb signatures and modules.

1|Updated Mar 11, 2026
Search Tags:#ai development#llm#ruby#type-safe#dspy#programmatic prompts
One-click install
npx skills add https://github.com/JimmyChen-NXP/zephyr-team-activity-dashboard --skill dspy-ruby-jimmychen-nxp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/JimmyChen-NXP/zephyr-team-activity-dashboard/tree/main/.github/skills/dspy-ruby
Command: npx skills add https://github.com/JimmyChen-NXP/zephyr-team-activity-dashboard --skill dspy-ruby-jimmychen-nxp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the development of robust, type-safe LLM applications in Ruby, moving beyond simple prompt engineering to a more programmatic and maintainable approach.

Core Features & Use Cases

  • Type-Safe Signatures: Define LLM interfaces with Sorbet types for predictable inputs and outputs.
  • Modular Components: Build complex AI workflows by composing reusable DSPy modules and tools.
  • Automated Optimization: Improve prompt performance using data-driven techniques like MIPROv2 and GEPA.
  • Use Case: Develop an AI-powered customer support agent that can understand user queries, access a knowledge base via tools, and provide structured, accurate responses, all within a Ruby application.

Quick Start

Use the dspy-ruby skill to create a new sentiment analysis signature.

Frequently Asked Questions about dspy-ruby

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

FAQPage Schema
How do I build type-safe LLM applications in Ruby?▼

To build type-safe LLM applications in Ruby, you can use DSPy.rb to define LLM interfaces with Sorbet types, ensuring predictable inputs and outputs for robust AI workflows.

What is programmatic prompt engineering and how does it work with Ruby?▼

Programmatic prompt engineering uses code to structure and optimize prompts. In Ruby, DSPy.rb facilitates this by composing reusable modules and integrating LLM providers via RubyLLM.

How do I optimize LLM prompts using data-driven techniques?▼

You optimize LLM prompts using automated, data-driven techniques like MIPROv2 and GEPA, which are integrated into the DSPy.rb framework to improve prompt performance systematically.

Can I compose modular components to build AI agents in Ruby?▼

Yes, you can build complex AI agent systems in Ruby by composing reusable DSPy modules and tools, allowing agents to access knowledge bases and provide structured responses.

Does DSPy.rb work with Sorbet for type checking LLM inputs and outputs?▼

Yes, DSPy.rb works directly with Sorbet to define type-safe signatures, ensuring that inputs and outputs for LLM applications are predictable and structurally validated.

What is the best way to structure LLM signatures in a Ruby application?▼

The best way to structure LLM signatures in a Ruby application is by defining them with Sorbet types using DSPy.rb, enabling modular composition and automated prompt optimization.

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