AI Evaluation Is Part of the Product
Strong AI products are evaluated on the actual job users need done.
Useful evaluation layers include:
<ul>
<li>offline task checks</li>
<li>human review against rubrics</li>
<li>online product metrics after launch</li>
</ul>
Without evaluation, teams tend to confuse demos with product quality.

What makes the strongest evaluation set for an AI feature?

A higher model benchmark score automatically means a better product experience.

Measure More Than Answer Quality
PMs should usually track some combination of:
<ul>
<li>task success or acceptance rate</li>
<li>edit rate or intervention rate</li>
<li>latency</li>
<li>cost per successful outcome</li>
<li>refusal and failure patterns</li>
<li>user trust or satisfaction</li>
</ul>
A model can be "smart" and still be a poor product choice.

If the highest-quality model is too slow for live chat and too expensive for scale, what should the PM conclude?

AI evaluation should include failure cases and edge cases, not just happy-path examples.

Launch with Feedback Loops
After launch, teams should instrument how users interact with the output:
<ul>
<li>accepted as-is</li>
<li>lightly edited</li>
<li>heavily edited</li>
<li>ignored</li>
<li>escalated to human support</li>
</ul>
Those signals often reveal more than a generic thumbs-up metric.

For an AI drafting feature, which launch metric is especially useful?

If evals are not tied to a user task and a decision threshold, they will be hard to act on.