Inspecting metrics like adoption, engagement, and retention is essential to understand how users interact with an AI product and its effectiveness.

When product teams focus solely on metrics like engagement or dwell time, they risk neglecting qualitative aspects such as content quality and user satisfaction, which can lead to long-term retention issues.

We must be cautious about how we define metrics for AI systems to avoid unintended consequences, such as optimizing for the wrong outcomes.

Many organizations focus on high-level performance metrics, which can mask undesired behaviors in AI systems, leading to a lack of robustness.

Meta AI's benchmark for success is user value, focusing on what users report as valuable rather than solely on traditional benchmarks, which can be skewed and not representative of actual user needs.

I value metrics like time on site and engagement over page views, as they indicate how well content resonates with the audience.

Using AI to summarize user behavior and engagement can enhance the user experience and provide valuable insights for improvement.

Key metrics for evaluating AI agents include deflection rate, which measures how many conversations the AI can handle without human escalation, and customer satisfaction scores, which reflect user experience.

Behavioral analysis of AI systems should include not just performance metrics but also characteristics like toxicity, reading level, and response time.