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dspy-ruby

Develop LLM applications in Ruby with type-safe DSPy.rb signatures.

11|Updated Jun 19, 2013
Search Tags:#ai#llm#ruby#dspy#programmatic ai#dspyrb
One-click install
npx skills add https://github.com/MadBomber/experiments --skill dspy-ruby-madbomber
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/MadBomber/experiments/tree/main/ai_misc/skills/dspy-ruby
Command: npx skills add https://github.com/MadBomber/experiments --skill dspy-ruby-madbomber

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill transforms how you build LLM applications in Ruby by enabling programmatic control over AI models, moving beyond manual prompt engineering to a more robust, testable, and maintainable approach.

Core Features & Use Cases

  • Type-Safe Signatures: Define clear input/output contracts for LLM operations.
  • Composable Modules: Build reusable, chainable AI components.
  • Multi-Predictor Support: Choose from Predict, ChainOfThought, ReAct, and CodeAct.
  • LLM Provider Agnostic: Easily configure OpenAI, Anthropic, Gemini, Ollama, and more.
  • Multimodal Capabilities: Process images alongside text.
  • Testing & Optimization: Write RSpec tests and optimize prompts automatically.
  • Observability: Integrate with OpenTelemetry and Langfuse for monitoring.
  • Use Case: Develop a customer support chatbot that classifies incoming emails, extracts key information, and generates personalized responses, all managed through type-safe DSPy.rb modules.

Quick Start

Configure DSPy.rb to use the OpenAI API with your API key.

Frequently Asked Questions about dspy-ruby

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

FAQPage Schema
How do I program LLM applications in Ruby instead of manually writing prompts?▼

You can program LLMs in Ruby by defining type-safe signatures and composable modules using the DSPy.rb framework, which replaces manual prompt engineering with testable, maintainable code.

Does DSPy.rb support multiple LLM providers like OpenAI, Anthropic, and Ollama?▼

DSPy.rb is LLM provider agnostic, supporting seamless configuration and integration with OpenAI, Anthropic, Gemini, and Ollama for your Ruby AI applications.

What is the best way to build a ReAct agent or Chain-of-Thought reasoning in Ruby?▼

Building Chain-of-Thought or ReAct agents in Ruby is done by selecting these composable multi-predictor modules within the DSPy.rb framework to structure your LLM's reasoning process.

Can I use RSpec to test and optimize LLM prompts in a Ruby application?▼

You can test and optimize LLM prompts by writing standard RSpec tests against your DSPy.rb modules, enabling automated prompt optimization and robust AI development.

How do I process images alongside text for multimodal LLM tasks in Ruby?▼

Multimodal processing of images and text is supported natively by defining the appropriate inputs in your DSPy.rb type-safe signatures.

How do I monitor and observe LLM application behavior in Ruby with OpenTelemetry?▼

You can achieve observability for your Ruby LLM applications by integrating DSPy.rb with OpenTelemetry and Langfuse to monitor module execution and performance.

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