DSPy for Ruby
Builds type-safe, composable, and optimizable LLM applications in Ruby using the DSPy.rb framework.
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Key features
- Configure and use multiple LLM providers, including OpenAI, Anthropic, Gemini, and Ollama.
- Create type-safe input/output contracts for LLM operations using Signatures.
- Implement testing strategies and automatically optimize prompts using techniques like MIPROv2.
- Build reusable, chainable Modules to create complex multi-step workflows.
- Develop agents that can use external tools to perform complex tasks using predictors like ReAct.
Use cases
- Building and testing a reliable content generation feature with structured, predictable outputs.
- Developing a multi-step AI pipeline for document analysis in a Rails application.
- Creating a customer support agent that can use external APIs to look up order information.
FAQ
How does this skill improve my workflow?
It accelerates your development by providing accurate code snippets, best practices, and quick references for DSPy.rb concepts. This allows you to focus on application logic rather than boilerplate code, making it faster to build, test, and deploy robust LLM-powered features.
What does the DSPy for Ruby skill do?
This skill provides expert guidance and code generation for DSPy.rb, a Ruby framework for building structured and optimizable LLM applications. It helps you program LLMs by creating type-safe, composable, and testable components.
When should I use this skill?
Use this skill when you are developing AI features in a Ruby application with DSPy.rb. It's ideal for creating predictable AI workflows, implementing agents with tools, configuring LLM providers like OpenAI or Anthropic, and optimizing prompts for performance.
What are its main capabilities?
The skill has deep knowledge of creating type-safe Signatures, building reusable Modules, configuring multiple LLM providers (OpenAI, Anthropic, Gemini, Ollama), implementing tool-using agents with ReAct, and automatically optimizing prompts using techniques like MIPROv2.
Can this skill help with testing and optimization?
Yes, it provides patterns for writing RSpec tests for your LLM logic and guides you on how to use optimizers like MIPROv2 to automatically improve your prompts based on training data and evaluation metrics, ensuring your application is both reliable and effective.
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