Week 4 (Thursday, shared with the Engineering cohort): Evals, the TRACE loop
7 lessons · Back to full syllabus
What you keep
How to evaluate an AI feature like a leader, using TRACE. Error analysis is product work, you own the front of this loop.
You ship
Your product, evaluated, with a clear read on where it stands before the sprint.
Lessons
The vibe-check trap
"It looked good when I tried it" is not evaluation, and vibes do not survive change.
TRACE: Trace and Read (error analysis is your job)
Capture real interactions, read them one by one, and journal what went wrong, product work, not engineering.
TRACE: Analyze (decide what matters, fix the obvious)
Cluster failures by frequency, fix the cheap ones, and judge pass/fail, not vague scores.
What good evals tooling looks like (so you can lead it)
Codify and Enforce are engineering, but you can recognise good checks and hold a team to them.
Build a simple must-pass checklist for your product
Turn top failures into binary pass/fail cases, and re-run the list every time you change the product.
How to brief an engineer to build the evals you need
Hand traces and must-pass cases, not a request for generic quality metrics.
Run TRACE on your product
Traces read, failures ranked, must-pass checklist built, at least one fix shipped.
Lessons in this module