Is your AI actually being used, or just switched on?
"It is live" is not an answer to "is it working?" Something can be deployed, busy, and quietly failing. This lesson is the analytics discipline that tells the difference: which numbers matter, how they get calculated, and how to spot the ones that flatter you.
Read a usage dashboard and say, with reasons, whether the thing is genuinely working or just generating activity.
Lesson 4 on evaluation. No database or programming experience needed: the code here is for reading, not writing.
Plain-English mode
Each section opens with a short summary before the detail. Turn this off once the vocabulary feels familiar.
A feature can be deployed, handling thousands of requests, and still failing. Total usage is the number most likely to mislead you.
One idea carries this whole lesson: live is not the same as adopted. Something can be deployed, receive thousands of requests a week, and be failing at what it was built to do.
Total usage is the most flattering and least informative number available. It rises when a handful of enthusiasts use it constantly. It rises when the thing fails and people retry. It rises when somebody wires it into an automated process nobody reads the output of. None of that tells you it is helping anyone.
The numbers that actually mean something are: how many distinct people use it, not how many times it was called; what proportion of the people who could use it actually do; how deeply they use it; and whether they come back. All of them broken down by group, because a healthy overall figure can conceal a system thriving with one team and dying with another.
The rest of this lesson, including the queries and the statistics, is part of the paid course.
Answer in your head first, then reveal. Getting it wrong is the part that makes it stick.
Usage of a new AI feature has tripled in a month. Name three explanations that are not "it is working".
The rest of this lesson
- Enough SQL to read a query
- The four questions worth asking
- Checking a difference is real
- The rule for deciding to expand
- One worked example: proving permit automation works
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