dspy-ruby

Name: dspy-ruby
Availability: InStock
Author: danfrdn

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

Updated Feb 17, 2026

Search Tags:#ai development #llm #ruby #type-safe #dspy #programmatic prompts

Ready to run

npx skills add https://github.com/danfrdn/antigravity-config --skill dspy-ruby-danfrdn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/danfrdn/antigravity-config/tree/main/skills/dspy-ruby
Command: npx skills add https://github.com/danfrdn/antigravity-config --skill dspy-ruby-danfrdn

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the development of robust, type-safe applications powered by Large Language Models (LLMs) using Ruby, moving beyond simple prompt engineering to a more programmatic and maintainable approach.

Core Features & Use Cases

Type-Safe Signatures: Define LLM inputs and outputs using Sorbet types for compile-time safety.
Modular Components: Compose LLM logic using DSPy modules like Predict, ChainOfThought, and ReAct agents.
Automatic Optimization: Improve prompt accuracy and efficiency using built-in optimization techniques.
Use Case: Develop an AI-powered customer support chatbot where user queries are classified, relevant information is retrieved using tools, and a coherent, type-safe response is generated.

Quick Start

Use the dspy-ruby skill to classify the user's request using a predefined 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?▼

You can build type-safe LLM applications in Ruby by defining inputs and outputs using Sorbet types for compile-time safety. This approach uses modular components to compose logic, ensuring robust and maintainable prompt engineering.

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

Programmatic prompt engineering replaces manual string formatting with modular, composable code structures. It works by defining explicit input-output signatures and using built-in optimization techniques to automatically improve prompt accuracy and efficiency.

Can I use RubyLLM to integrate multiple LLM providers in my application?▼

Yes, you can use RubyLLM to integrate multiple LLM providers. The framework supports various LLM providers via adapters, allowing you to connect and switch between different models within your type-safe Ruby application.

How do I create agent-based systems with ReAct and ChainOfThought modules?▼

You create agent-based systems by composing LLM logic using modular components like Predict, ChainOfThought, and ReAct agents. These modules allow your application to classify queries, retrieve information using tools, and generate coherent responses.

Does this Ruby LLM framework support automated prompt optimization?▼

Yes, the framework supports automated prompt optimization. You can improve prompt accuracy and efficiency using built-in optimization techniques that automatically refine and enhance the programmatic prompts within your application.

What is the best way to structure an AI customer support chatbot in Ruby?▼

The best way to structure an AI chatbot in Ruby is using modular LLM components. You can classify user requests with predefined signatures, retrieve relevant information using agent tools, and generate type-safe, coherent responses.