Part 5/10: How to Evaluate AI Systems Like a Real AI PM

If you've ever shipped a machine learning feature and watched it perform beautifully on your test set only to crumble in production, you already understand the core thesis of this post: evaluation is the most underrated competency in AI product management. It's also the one that separates PMs who merely manage AI projects from those who genuinely own AI product quality.

In this fifth installment of our AI PM series, we'll go deep into the mechanics of evaluating AI systems not from a data scientist's vantage point, but from the product manager's. You'll learn why accuracy is a deceptive metric, how to build a rigorous evaluation framework, how to define a North Star metric for AI products, the critical distinction between offline and online evaluation, and how to implement guardrails that prevent catastrophic failures.

Let's get technical.