Create type-safe LLM applications in Ruby with DSPy modules.
npx skills add https://github.com/danfrdn/antigravity-config --skill dspy-ruby-danfrdn
Please help me install this Agent Skill. Skill: dspy-ruby Source: https://github.com/danfrdn/antigravity-config/tree/main/skills/dspy-ruby Command: npx skills add https://github.com/danfrdn/antigravity-config --skill dspy-ruby-danfrdn
This Skill simplifies the development of robust, type-safe applications powered by Large Language Models (LLMs) using Ruby, moving beyond simple prompt engineering to a more programmatic and maintainable approach.
Predict, ChainOfThought, and ReAct agents.Use the dspy-ruby skill to classify the user's request using a predefined signature.
High-intent search queries and answers about installing and using this skill.
You can build type-safe LLM applications in Ruby by defining inputs and outputs using Sorbet types for compile-time safety. This approach uses modular components to compose logic, ensuring robust and maintainable prompt engineering.
Programmatic prompt engineering replaces manual string formatting with modular, composable code structures. It works by defining explicit input-output signatures and using built-in optimization techniques to automatically improve prompt accuracy and efficiency.
Yes, you can use RubyLLM to integrate multiple LLM providers. The framework supports various LLM providers via adapters, allowing you to connect and switch between different models within your type-safe Ruby application.
You create agent-based systems by composing LLM logic using modular components like Predict, ChainOfThought, and ReAct agents. These modules allow your application to classify queries, retrieve information using tools, and generate coherent responses.
Yes, the framework supports automated prompt optimization. You can improve prompt accuracy and efficiency using built-in optimization techniques that automatically refine and enhance the programmatic prompts within your application.
The best way to structure an AI chatbot in Ruby is using modular LLM components. You can classify user requests with predefined signatures, retrieve relevant information using agent tools, and generate type-safe, coherent responses.
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gvkhoslaBuild type-safe LLM applications in Ruby with programmatic prompts and Sorbet signatures.