Can I use DSPy.rb with Ruby on Rails?

Absolutely. It follows Rails conventions and includes patterns for organizing signatures, modules, and tools within a standard Rails directory structure like app/signatures and app/modules.

Which LLM providers are supported?

It supports major providers like Anthropic, OpenAI, and Gemini via dedicated adapters, as well as local models via Ollama.

How does prompt optimization work in DSPy.rb?

It uses algorithms like MIPROv2 and GEPA to iteratively refine instructions and examples based on evaluation data, moving away from 'guess-and-check' prompt engineering.

DSPy.rb is a Ruby framework that allows you to build LLM applications programmatically using signatures and modules instead of manual prompt strings.

Do I need to use Sorbet with DSPy.rb?

Yes, DSPy.rb leverages Sorbet for type-safe signatures, ensuring your LLM inputs and outputs conform to specific data structures and enums.

DSPy.rb for Claude Code

Name: DSPy.rb for Claude Code
Author: vicentereig

byvicentereig

•

데이터 과학 및 ML

Builds type-safe, modular LLM applications using Ruby's programmatic prompt framework with signatures and automated optimization.

DSPy.rb brings software engineering rigor to LLM development by replacing brittle prompt engineering with declarative Ruby signatures and modular components. It leverages Sorbet for strict type safety, allowing developers to define clear input/output interfaces, compose complex AI workflows through modules like ChainOfThought and ReAct, and automatically optimize prompts using data-driven techniques like MIPROv2 and GEPA. Designed for production, it integrates seamlessly with Ruby on Rails and supports a wide array of providers including Anthropic, OpenAI, and Gemini.

주요 기능

01Rails-style lifecycle callbacks for instrumentation and telemetry

02Type-safe signatures using Sorbet for robust LLM input/output validation

03Modular AI components including Predict, ChainOfThought, and ReAct agents

04Automatic prompt optimization using MIPROv2 and Genetic-Pareto evolution

051 GitHub stars

06Multi-provider support for OpenAI, Anthropic, Gemini, and local Ollama models

사용 사례

01Optimizing LLM prompt performance using systematic, data-driven evaluation frameworks

02Building complex, multi-step AI agents with type-safe tool-calling capabilities

03Developing production-grade Ruby on Rails applications with integrated LLM logic

주요 기능

01Rails-style lifecycle callbacks for instrumentation and telemetry

02Type-safe signatures using Sorbet for robust LLM input/output validation

03Modular AI components including Predict, ChainOfThought, and ReAct agents

04Automatic prompt optimization using MIPROv2 and Genetic-Pareto evolution

051 GitHub stars

06Multi-provider support for OpenAI, Anthropic, Gemini, and local Ollama models

사용 사례

01Optimizing LLM prompt performance using systematic, data-driven evaluation frameworks

02Building complex, multi-step AI agents with type-safe tool-calling capabilities

03Developing production-grade Ruby on Rails applications with integrated LLM logic