First, You Have to Look
Before you can measure well, you have to look — read the real traces and outputs your system produces and see how it actually fails. The failures you find tell you what is worth measuring.
How Error Analysis Works
A simple, repeatable loop — the goal is to spend your effort where it changes the most.
- Collect real outputs from your running system
- Read a sample of them, one by one
- Group the failures into categories
- Count each category, then fix the biggest bucket first
Measure What Matters
Combine what you see in the data with product and business insight. Measure what matters to the user — not just what is easy to count.
The Data Names Your Metrics
Exploratory analysis of real traces beats guessing at metrics up front.
- Guessing metrics in a meeting rarely matches how the system really fails
- Reading traces surfaces failure modes you would never have listed
- Each failure you find names a metric you now know to track
A Few Real Examples
A handful of concrete cases beats a vague average. One read-through of ten bad outputs teaches you more than a dashboard of numbers — you see the actual mistake, in context, in the model's own words.
Build It
How to implement: collect 10–20 real outputs from your tool, read them, and write down the failure categories you see — and which one is most common.
- Weekly AI Tasks tracker — categorize parse failures (wrong date, wrong task, missed status) and fix the biggest category.
- Personal brand site — categorize copy problems (unsupported claim, wrong tone, missing project) and fix the worst first.
What you learned
Look before you measure. Error analysis is a loop — collect, read, group, count, fix the biggest bucket. Measure what matters to the user, and let a few real examples guide you more than any average.