In this episode, Hamel Husain and Shreya Shankar dive into the intricacies of AI evaluation (evals), discussing their importance in AI development and the nuances of teaching these concepts. Husain and Shankar, both experienced in AI and data science, explore how evals can bridge the gap between demo products and fully functional AI applications.
The Importance of Evals in AI Development
- “Anytime I try to help someone build an AI application, they always get stuck on how to move beyond a demo product.”
- “People really get stuck on this systematic measurement and evaluation of AI, which is really important.”
- Evals are crucial for transitioning AI from demo to production, providing a systematic way to measure and improve AI applications.
- Many AI engineers lack the data literacy needed for effective evals, highlighting a gap in current AI education.
- The process of evals is stable and evergreen, focusing on data literacy and analysis rather than specific tools or APIs.
Teaching Evals: Challenges and Strategies
- “We want to teach the subject; we don't want it to become a carnival.”
- “The syllabus breaks down the evals lifecycle from creating synthetic data to error analysis.”
- The course aims to provide hands-on experience with evals, including coding projects and live coding sessions.
- Emphasizes the importance of understanding data generation and evaluation beyond simple reliance on LLMs.
- The course is designed to be evergreen, focusing on stable techniques and principles that remain relevant over time.
Trends and Innovations in Evals
- “There's a really high payoff to building your own application that lets you annotate your data.”
- “People love LLM as a judge, but you really have to make sure that you can trust the LLM as a judge.”
- Custom applications for data annotation can significantly enhance the eval process by tailoring it to specific domain needs.
- LLMs as judges are popular but require careful validation against domain experts to ensure reliability.
- The field is evolving with new tools and methods, but core principles of evals remain consistent.
Key Takeaways:
- Evals are essential for moving AI applications from demo to production, requiring systematic measurement and data literacy.
- Teaching evals involves hands-on, practical approaches to ensure understanding and application of stable, evergreen techniques.
- Custom data annotation tools and careful validation of LLMs as judges are crucial for effective evals.
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