🦄 Evals for Classification
In this episode of AI That Works, hosts Vaibhav Gupta and Dex, along with guest Kevin Gregory, explore the intricacies of building AI systems that are ready for production. They discuss the concept of dynamic UIs, the challenges of large-scale classification, and the importance of user experience in AI applications. The conversation delves into the use of LLMs for enhancing classification systems, the evaluation and tuning of these systems, and the subjective nature of what constitutes a 'correct' classification. The episode emphasizes the need for engineers to focus on accuracy and user experience while navigating the complexities of AI engineering. The speakers also discuss model upgrades, user feedback, and the importance of building effective user interfaces, emphasizing iterative development and rapid prototyping for chatbot performance evaluation.
Project Details
Open in GitHubIn this episode, hosts Vaibhav Gupta and Dex, along with guest Kevin Gregory, explore building production-ready AI classification systems, focusing on evaluation, tuning, and user experience design.
Video (1h27m)
Episode Overview
This episode dives deep into the practical challenges of building AI systems ready for production. The hosts explore large-scale classification systems handling 1000+ categories, demonstrating how to evaluate and tune these systems for real-world use.
Key Topics Covered
- Building production-grade classification systems
- Dynamic UIs for flexible content creation
- Using LLMs to enhance classification accuracy
- Evaluation strategies and custom dashboards
- The subjective nature of classification correctness
- Tuning classification pipelines for performance
- Balancing accuracy, cost, and user experience
Key Takeaways
- AI engineering concepts can be applied to real projects with measurable impact
- Building production-grade classification systems requires careful attention to UX
- Evaluating AI systems requires understanding both metrics and user experience
- Subjectivity plays a significant role in defining correct classifications
- Real user data is crucial for effective iteration and improvement
- UI design should prioritize clarity and enable rapid spot-checking
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