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dspy-ruby

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

Updated Feb 10, 2026
Search Tags:#prompt engineering#ai development#llm#ruby#type-safe#dspy
One-click install
npx skills add https://github.com/sammyjoyce/pi-compound-engineering --skill dspy-ruby-sammyjoyce
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/sammyjoyce/pi-compound-engineering/tree/main/skills/dspy-ruby
Command: npx skills add https://github.com/sammyjoyce/pi-compound-engineering --skill dspy-ruby-sammyjoyce

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 LLM applications in Ruby by providing a programmatic framework for defining prompts, composing modules, and optimizing LLM interactions.

Core Features & Use Cases

  • Type-Safe Signatures: Define LLM interfaces with Sorbet types for predictable inputs and outputs.
  • Modular Components: Build complex LLM workflows by composing reusable signatures, modules, and tools.
  • Automatic Optimization: Leverage data to improve prompts and model performance without manual tweaking.
  • Use Case: Develop a customer support chatbot that uses type-safe signatures to classify incoming queries, extract relevant information, and generate structured responses, ensuring data integrity and predictable behavior.

Quick Start

Use the dspy-ruby skill to build a sentiment analysis application by defining a SentimentAnalysis signature and using the Predict module.

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 applications in Ruby by defining LLM interfaces with Sorbet types and composing modular components like Predict and ChainOfThought to ensure predictable inputs and structured outputs.

What is the best way to optimize LLM prompts programmatically in Ruby?▼

You can optimize LLM prompts programmatically in Ruby by leveraging data-driven techniques like MIPROv2 and GEPA to automatically improve prompt quality and model performance without manual tweaking.

Does this Ruby LLM framework support integrating external tools with reasoning modules?▼

Yes, the framework supports integrating external tools with reasoning modules like ReAct, allowing you to build complex LLM workflows by composing reusable signatures, modules, and tools.

Can I use RubyLLM to connect multiple LLM providers for my type-safe signatures?▼

Yes, you can connect various LLM providers via RubyLLM to supply the underlying model interactions for your type-safe signatures, while also utilizing built-in observability features for tracing and evaluation.

How do I define structured LLM inputs and outputs for a Ruby AI application?▼

You define structured LLM inputs and outputs by creating type-safe signatures using Sorbet, which acts as a programmatic interface to guarantee data integrity and predictable behavior across your application.

Why should I use Sorbet types for prompt engineering in Ruby?▼

Using Sorbet types for prompt engineering in Ruby ensures data integrity and predictable behavior by enforcing strict type checks on LLM signatures, preventing misaligned inputs and outputs in complex workflows.

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