A collection of AI prompts, examples, and research for Ruby developers who want to use AI tools with intention and purpose.
- Survey Analysis - What 42 Ruby developers think about AI tools
- Getting Started Guide - Introduction to AI tools for Ruby
- Code Generation Prompts - Templates for generating Ruby/Rails code
- Research-Based Techniques - Evidence-backed prompting strategies
- Community Resources - Tools, articles, and further reading
- Survey Analysis - Full community survey findings
- NIST Explainable AI - Applying NIST principles to Ruby development
This repository contains practical resources for Ruby developers using AI coding tools:
Prompts - Ready-to-use templates for:
- Code generation (models, controllers, services)
- Testing (RSpec, test cases)
- Refactoring and debugging
- Documentation (YARD, READMEs)
Examples - Before/after comparisons showing effective prompting
Research - Survey data from 42 Ruby developers and scientific backing
Guides - How to use AI tools thoughtfully in Ruby development
prompts/ Organized by task (generation, testing, refactoring)
examples/ Real-world before/after comparisons
guides/ Learning resources and best practices
resources/
└── research/ Survey analysis, NIST framework, context switching
templates/ Reusable prompt templates
Full structure details: STRUCTURE.md
AI as augmentation, not replacement
- You remain the Ruby expert
- AI handles boilerplate and initial drafts
- Always verify and understand AI output
Ruby-first approach
- Maintain readability and expressiveness
- Follow Ruby idioms and Rails conventions
- Preserve developer happiness
Research-backed practices
- Community survey insights (42 developers)
- NIST explainability framework
- Understanding automation bias and context switching costs
- GitHub Copilot - Code completion, integrated into editors
- Cursor - AI-native editor with project context
- Claude Code - Terminal-based assistant (used to create this talk)
- Continue.dev - Open-source, customizable
- ChatGPT/Claude - Code review and analysis
See tools comparison for detailed analysis.
Based on 42 Ruby developer responses:
Adoption: 38% daily users, 33% non-users, 19% moderate users
Ruby-specific challenges:
- Metaprogramming: 2.1/5 (AI's weakest area)
- Rails conventions: 3.3/5 (moderate)
- Blocks/iterators: 2.5/5 (below average)
Top use cases: Code generation, writing tests, debugging, documentation
Community wisdom: "Let it rough draft, and you refine" - Always verify AI output
Full analysis: Survey Results
Works well:
- Rails scaffolding and CRUD operations
- RSpec test skeletons
- Documentation and comments
- Debugging assistance
- Research and exploration
Use with caution:
- Metaprogramming and DSLs (AI struggles significantly)
- Security-sensitive code
- Complex business logic
- Architectural decisions
- Custom Rails patterns
- Read the Survey Analysis to understand community experiences
- Browse Code Generation Prompts for practical templates
- Check Research-Based Techniques for evidence-backed strategies
- Try examples from workflows in your own projects
Contributions welcome! See CONTRIBUTING.md for guidelines.
What we're looking for:
- Prompts that work well for Ruby/Rails
- Before/after examples showing effectiveness
- Real-world workflows and case studies
- Tool experiences and recommendations
This repository supports the "Thoughtful AI for the Rubyist" conference talk, created to help Ruby developers use AI tools thoughtfully while maintaining Ruby's human-centered philosophy.
Key message: AI can enhance Ruby development when guided by Ruby values and your professional expertise. You're the expert—AI is just a tool.
Part of the "Thoughtful AI for the Rubyist" community resource collection