dspy-ruby

You can build type-safe LLM applications in Ruby by defining programmatic prompt signatures using Sorbet types, ensuring predictable inputs and outputs for your language model features.

Programmatic prompt development replaces manual prompt strings with modular components like Predict, ChainOfThought, and ReAct, allowing you to compose LLM logic and automatically optimize prompt performance using data-driven techniques.

Yes, you can configure various language model providers for your Ruby AI application by using built-in adapters or integrating RubyLLM to route your type-safe LLM signatures.

You can optimize LLM prompt performance automatically using built-in optimization frameworks that apply data-driven techniques like MIPROv2 and GEPA to improve your programmatic prompts.

Yes, this Ruby LLM framework supports agent system development with tools, allowing you to build chatbots that classify intent, extract information, and provide structured responses using type-safe modules.

The best way to test LLM-powered functionality in Ruby is using the built-in observability, lifecycle hooks, and testing frameworks designed for predictable, enterprise-ready AI feature validation.