AI Engineer World's Fair 2025 - Evals Introduction to Evals 00:00
The session focuses on the importance of evaluations (evals) in AI systems, especially in the context of emerging generative AI technologies.
The speaker emphasizes the need for companies to rapidly adapt their products to new AI models and incorporate user feedback effectively.
The Evolution of AI Eval Practices 02:00
Prior to the launch of ChatGPT, machine learning monitoring was often disconnected from business needs.
The introduction of generative AI technologies has shifted the conversation, leading to increased interest from CEOs and CFOs in AI evaluation.
Key Signs of Effective Evals 06:45
Successful organizations can quickly incorporate new AI models into their products, ideally within 24 hours.
Companies should have a clear process for converting user complaints into actionable evals to continuously improve their systems.
Engineering Great Evals 10:30
Evals must be purposefully engineered rather than relying on synthetic data or generic scoring systems.
The importance of aligning datasets with user experiences is highlighted, advocating for continuous reconciliation of data with real-world scenarios.
Importance of Context in AI 14:00
There is a need to consider how tools within AI systems are defined and how their outputs are formatted for optimal performance.
Effective scoring systems should include both code-based and LLM-as-judge approaches, tailored to specific applications.
Preparing for New AI Models 18:30
Organizations should be ready to pivot when new AI models are released, ensuring that their systems can quickly adapt to leverage the latest advancements.
System Optimization for Evals 22:00
Enhancing the entire AI system, including tasks and scoring functions, is crucial for improving eval performance.
The speaker discusses a new feature called "Loop," which auto-optimizes eval tasks using AI, simplifying the process of improving AI applications.
Conclusion 25:45
The final messages emphasize using evals to drive product development decisions based on user data and feedback.
The session concludes with an invitation for further questions and discussion on the topic.
Q&A Session 27:30
Attendees engage with the speaker, asking questions about specific applications of evals, the integration of human feedback, and the future direction of AI evaluation practices.