dspy-ruby

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

5|1|Updated Aug 26, 2025

Search Tags:#prompt engineering #llm #agent #ruby #type-safe #dspy

Ready to run

npx skills add https://github.com/hollandkevint/thinkhaven --skill dspy-ruby-hollandkevint

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the development of Large Language Model (LLM) applications in Ruby by providing a programmatic, type-safe framework that emphasizes software engineering best practices.

Core Features & Use Cases

Type-Safe Signatures: Define LLM interfaces using Sorbet types for robust input/output validation.
Modular Components: Compose LLM logic using reusable modules like Predict, ChainOfThought, and ReAct agents.
Automatic Optimization: Improve prompt accuracy using data-driven optimization techniques.
Tool Integration: Build agents that can interact with external tools and APIs in a type-safe manner.
Use Case: Develop a customer support chatbot that can classify incoming emails, retrieve relevant information from a knowledge base using a search tool, and generate a concise, helpful response, all within a type-safe Ruby environment.

Quick Start

Use the dspy-ruby skill to classify an email by providing its content.

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?▼

Build type-safe LLM apps in Ruby by using the DSPy.rb framework to define LLM interfaces with Sorbet types, enabling robust input and output validation for modular components.

What is programmatic prompt engineering in Ruby?▼

Programmatic prompt engineering in Ruby uses modular components like Predict, ChainOfThought, and ReAct agents to compose LLM logic, replacing static prompt strings with reusable, testable modules.

How do I build LLM agents with tool integration in Ruby?▼

Build LLM agents with tool integration in Ruby using the ReAct component to interact with external tools and APIs in a type-safe manner, ensuring robust input and output validation.

Does dspy-ruby support automated prompt optimization?▼

Yes, dspy-ruby supports automated prompt optimization by applying data-driven optimization techniques to improve prompt accuracy and enhance overall LLM application performance.

Can I use RubyLLM as a unified adapter for LLM providers?▼

Yes, you can use RubyLLM as a unified adapter for LLM providers, or integrate with various LLM providers via adapter gems to connect your type-safe Ruby LLM applications.

What are the limitations of using Sorbet types for LLM interfaces in Ruby?▼

Using Sorbet types for LLM interfaces requires strict type definitions for input and output validation, which may increase initial setup complexity but ensures robust type safety.