Diagnosing Failures and Finding Root Causes
Explore how to diagnose AI feature failures by examining detailed outputs, asking five critical questions to find root causes, and avoiding common mistakes. Understand how to identify whether a failure stems from incorrect labels, missing info, unclear prompts, capability gaps, or genuine model limits to make targeted improvements.
The previous two lessons built an evaluation set and a way to grade what comes back. Together they tell us which cases failed. Neither one tells us why, and the wrong fix for the wrong reason wastes effort without actually improving anything. This lesson builds the diagnostic process that turns a failing case into a specific, targeted fix.
In this lesson, we will cover:
Printing enough detail about a failure to actually diagnose it, beyond just counting it
Five questions that separate the real causes a failure can have
Where each cause actually gets fixed elsewhere in this course
How this course already did this diagnosis once, informally, back in the lifecycle lesson
How the same triage shows up in real products
The three ways engineers get this wrong
Seeing the failure in enough detail to diagnose it
A pass rate tells us a case failed. It doesn’t show us the conversation, what the model returned, or what we expected instead, the three things ...