Evaluating LLMs with Leva
Notas del episodio
In this episode of the Ruby AI Podcast, host Valentino Stoll talks with special guest Kieran, a prominent figure in the Ruby AI space. Kieran recently gave a talk at the San Francisco Ruby Meetup about his new gem, Leva, which focuses on LLM evaluations in Ruby. Kieran discusses his background, his passion for AI and Ruby, as well as his journey in building AI products, including his tool Cora, which helps manage email inboxes by categorizing and summarizing emails using AI. Together, Valentino and Kieran explore the process, challenges, and best practices of creating AI-driven gems and tools in Ruby, the importance of evaluations, and the fun and creative aspects of integrating AI into Ruby on Rails projects.
Mentioned in the show:
- Kieran Klaassen – Ruby developer, creator of Cora and Leva.
- Leva gem – Kieran's LLM evaluation framework for Rails.
- Jumpstart Pro – “is the best Ruby on Rails SaaS template out there”.
- Stepper / Stepper Motor (workflow engine) – a “journey” with steps for background jobs.
- Jaccard Index – A metric for set similarity (|A∩B|/|A∪B|).
- LangSmith – a platform for building production-grade LLM applications.
- Morph LLM – The Fastest Way to Apply AI Edits (4500+ tokens/sec).
- Friday AI Agent – An AI-powered coding agent that handles PRs from start to finish.
- DSPy.rb – Framework for building AI agents and optimizing prompts.
Highlights:
00:00 Introduction and Guest Welcome
00:53 Kieran's Background and AI Journey
01:20 Building AI Tools and the Leva Gem
03:47 Challenges and Best Practices in AI Development
07:16 Evaluations and Real-World Applications
07:36 Community Recognition and Adoption
12:37 Prompt Engineering and Model Testing
22:06 Leveraging AI for Workflow Optimization
28:35 Visualizing Workflows and Tools
31:44 Exploring Hybrid Orchestration Layers
33:15 Debating Deterministic Workflows vs. Agent Flows
34:28 The Fun of Experimenting with AI and Ruby
34:55 Building Gems and Learning Through Creation
40:03 The Value of Rails in AI Development
46:28 Evaluating AI Outputs and Metrics
50:40 Annotation and Continuous Improvement
53:50 Future of AI and Rails Integration
54:54 Closing Thoughts and Recommendations
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