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dspy-ruby

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

Updated Jun 28, 2022
Search Tags:#prompt engineering#ai development#llm#ruby#type-safe#dspy#sorbet
One-click install
npx skills add https://github.com/pcasaretto/nix-home --skill dspy-ruby-pcasaretto
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/pcasaretto/nix-home/tree/main/home-manager/modules/common/pi/compound-engineering/skills/dspy-ruby
Command: npx skills add https://github.com/pcasaretto/nix-home --skill dspy-ruby-pcasaretto

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 Large Language Model (LLM) applications within the Ruby ecosystem, moving beyond simple prompt engineering to a more structured, software-engineering approach.

Core Features & Use Cases

  • Type-Safe Signatures: Define LLM inputs and outputs using Sorbet types for compile-time and runtime safety.
  • Modular Components: Build complex LLM workflows by composing reusable modules and predictors.
  • Automatic Optimization: Leverage built-in optimizers like MIPROv2 and GEPA to improve prompt accuracy with data.
  • Agent Development: Create sophisticated agents using ReAct and CodeAct patterns with integrated tool usage.
  • Use Case: Develop a customer support chatbot that can understand user queries, classify intent, retrieve relevant information using tools, and generate a structured, type-safe response, all within a Ruby application.

Quick Start

Use the dspy-ruby skill to classify the sentiment of the provided text.

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 using the DSPy.rb framework, which defines LLM inputs and outputs with Sorbet types for compile-time and runtime safety. Compose modular components and predictors to structure complex workflows.

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

Programmatic prompt engineering replaces manual prompt strings with structured signatures and modules. In Ruby, DSPy.rb uses these signatures to define typed inputs and outputs, enabling modular composition and automatic optimization of prompts.

Can I use DSPy with Rails to create LLM agents with tool usage?▼

Yes, DSPy.rb integrates with Rails and supports sophisticated agent development using ReAct and CodeAct patterns. These agents include integrated tool usage for tasks like retrieving information and generating structured responses.

Do I need Sorbet to define type-safe signatures for LLM workflows?▼

Yes, Sorbet types are required to define type-safe signatures in DSPy.rb. Sorbet provides both compile-time and runtime safety for LLM inputs and outputs, ensuring structured data handling across modular LLM workflows.

What's the best way to optimize LLM prompt accuracy with data in Ruby?▼

Optimize LLM prompt accuracy in Ruby using built-in DSPy.rb optimizers like MIPROv2 and GEPA. These techniques automatically improve prompt accuracy by leveraging data-driven optimization over defined signatures and modules.

Does RubyLLM support multiple LLM providers for Ruby-based AI development?▼

Yes, RubyLLM integrates with various LLM providers for Ruby-based AI development. DSPy.rb uses this integration to connect workflows with different models while maintaining type-safe signatures and observability features.

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