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dspy-ruby

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

Updated Aug 23, 2026
Search Tags:#ai#llm#ruby#type-safe#dspy#programmatic prompts
One-click install
npx skills add https://github.com/TrendpilotAI/invesco-demo --skill dspy-ruby-trendpilotai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/TrendpilotAI/invesco-demo/tree/main/skills/compound-engineering/skills/dspy-ruby
Command: npx skills add https://github.com/TrendpilotAI/invesco-demo --skill dspy-ruby-trendpilotai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies and standardizes the development of complex 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 with Ruby types (Sorbet) for predictable inputs and outputs.
  • Modular Components: Compose LLM logic using signatures, modules, and agents.
  • Automatic Optimization: Improve prompts and models using data, not guesswork, with advanced optimization techniques.
  • Production-Ready: Built-in support for observability, testing, and error handling.
  • Use Case: Integrate advanced AI features into your Ruby application, such as intelligent document analysis, complex data generation, or agent-based task automation, with robust type checking and testing.

Quick Start

Use the dspy-ruby skill to analyze the sentiment of the text "This is an amazing product!".

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 LLM interfaces with type-safe signatures using Sorbet, ensuring predictable inputs and outputs. The framework provides modular components like agents and signatures to standardize complex AI development.

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

Programmatic prompt engineering replaces manual guessing with data-driven optimization techniques like MIPROv2 and GEPA. In Ruby, it works by composing modular LLM logic through signatures and agents to systematically improve prompts and models.

How do I optimize LLM prompts automatically instead of guessing?▼

You can optimize LLM prompts automatically using advanced techniques like MIPROv2 and GEPA provided by the framework. This allows you to improve prompt performance based on data rather than manual adjustments.

Can I use RubyLLM to integrate LLM providers in a type-safe Ruby application?▼

Yes, you can integrate various LLM providers via RubyLLM or direct adapters in your type-safe Ruby application. This integration supports features like lifecycle callbacks and fiber-local LM context for predictable AI development.

What is the best way to test and evaluate LLM agents in Ruby?▼

The best way to evaluate LLM agents in Ruby is using a comprehensive evaluation framework that supports built-in observability, testing, and error handling. This ensures predictable AI development and production-ready agent-based task automation.

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