dspy-ruby

You can build type-safe LLM applications in Ruby by defining interfaces between your application and the language model using Ruby types and Sorbet for type checking. This framework handles interface definition, modular component composition, and provider configuration.

Yes, you can configure multiple LLM providers using RubyLLM within your Ruby application. The framework supports provider configuration for predictive AI, allowing you to set up models like openai/gpt-4o-mini directly through the configuration block.

The best way to define LLM interfaces is by using type-safe signatures that specify Ruby types for inputs and outputs. This structured approach ensures predictable interactions for tasks like prediction, reasoning, and code generation.

Yes, Sorbet is required for type checking when using this Ruby prompt framework. It enforces type safety across your LLM signatures and modular components, ensuring reliable application behavior.

You optimize prompts for AI application development in Ruby by applying advanced techniques like multi-prompt optimization and genetic Pareto reflective prompt evolution. These methods refine predictive AI performance within modular component workflows.

Modular components for LLM workflows in Ruby include building blocks for prediction, reasoning, and code generation. These components compose together to handle complex agent systems and structured predictive tasks.