PM AI Evals
(2026 Edition)
5 eval layers and 5 rules for PMs shipping AI products.
Build AI Eval PM Skills — Free →5 Eval Layers
Golden dataset — small, curated, hand-labeled set; regression test every change
Synthetic evals — generated test cases covering edge modes
LLM-as-judge — another model grades outputs against a rubric; fast but biased
Human review — periodic expert audit; costly but grounds reality
Online metrics — real user outcomes; the ultimate check on offline evals
5 Rules
Every prompt change is a code change — version it, eval it, review it
Offline evals must predict online outcomes or they're noise
Track drift — model vendors update silently; your quality shifts without warning
Budget eval time like test time — aim for 10–20% of PM + eng cycles on evals
Never ship on vibes — 'the demo looks good' is not an eval
FAQ
What's the biggest eval mistake PMs make?
Relying only on anecdotal testing ('it worked when I tried it') instead of systematic evaluation. AI products fail probabilistically — meaning they can work 9 times and break the 10th. Without a golden dataset and regression testing, you don't know you've regressed until users tell you. By then trust is gone.