Trubrics CEO on Measuring Impact of AI Products

How do I know if my AI product is working? In this webinar hosted by Dan Hammaker, Trubrics CEO Jeff Kayne will explain the ins and outs of measuring impact of AI products. Learn about new metrics specific to AI products and best practices for tracking them from one of the pioneers in the space.

Join us for new conversations with leading product executives every week. Roll through the highlights of this week’s event below, then head on over to our Events page to see which product leaders will be joining us next week.

Show Notes:

1. Measuring the impact of AI-enabled capabilities is different from traditional software due to the probabilistic and stochastic nature of AI outputs.
2. Benchmarks are not a reliable way to select AI models, while evaluations (evals) are crucial for validating model performance.
3. Continuously evolving the evaluation data set to reflect real-world usage is important for improving AI models over time.
4. Analyzing user interaction patterns, such as reformulating questions, can provide valuable insights into model performance and user needs.
5. Capturing both explicit and implicit user feedback is important for understanding the user experience with AI-powered applications.
6. Aggregating metrics at the session level, rather than just individual interactions, can provide a more holistic view of user success.
7. Identifying when the AI model is unable to fulfill a request is a key metric for understanding model limitations and areas for improvement.
8. Integrating product manager insights into the prompt engineering process can help improve model performance.
9. Modifying guardrails and feeding user insights back into the evaluation data set are effective ways to iteratively improve AI-powered applications.
10. Starting with understanding the product experience without AI and identifying success metrics is crucial before integrating AI.
11. Leveraging public models and summarizing large quantities of data can provide quick insights into user interactions and model performance.
12. Associating user IDs with prompts and generations is important for tracking user interactions and identifying power users.
13. Real-time analytics and aggregated metrics on user sessions can provide valuable insights for improving AI-powered applications.
14. Tracking when the model apologizes or cannot fulfill a request is a powerful metric for understanding model limitations.
15. Analyzing user intents and topics that the model struggles with can inform product feature improvements.
16. In regulated industries, using product analytics to observe aggregated metrics without storing sensitive data is crucial for maintaining data privacy.
17. AI can help aggregate large quantities of text data, which may enable future advancements in product analytics.
18. The level of information extracted from user interactions with AI-powered applications can bring product managers closer to their users.
19. While the introduction of AI can be unsettling, it is important to build with the technology in a responsible manner.
20. AI has the potential to revolutionize product management, and continuous learning and adaptation are key to leveraging this technology effectively.