Monitoring Cost, Latency, and Quality Drift
Understand how to detect and address cost increases, latency slowdowns, and quality drift in AI product features. Learn to build integrated monitoring metrics that keep your AI systems reliable over time without errors signaling decline. Explore common pitfalls and how continuous evaluation ties into effective AI feature lifecycle management.
We'll cover the following...
The previous lesson handled failures that announce themselves, a timeout, a dropped connection, something a person can see happen. This lesson covers the opposite problem. Nothing crashes, nothing logs an error, and the feature is measurably worse than it was a month ago anyway. Closing this course’s loop means watching for exactly this, since it’s the one kind of failure that reliability work will never catch. In this lesson, we will cover:
Why drift never shows up as an error
Three concrete ways cost, latency, and quality drift in this course’s own running example
Building metrics into the evaluation harness instead of a separate system
How monitoring closes the loop this course opened in its second lesson
How this shows up in a real product
The three ways engineers get this wrong
Why drift never shows up as an error
Every failure this course has handled so far announces itself somehow, a validation error, a timeout, a confidently wrong answer a human catches on review. Drift doesn’t announce anything. A prompt that grows a little every time someone patches an edge case still ...