Why should AI product teams treat evaluation as part of the product, not an afterthought?
OpenAI Platform Docs · Jul 2, 2026, 4:30 p.m.
AI products need practical evaluation loops that test answer quality, user trust, edge cases, and human review behavior rather than only relying on model capability claims.
Why it matters
Evaluation design helps teams separate impressive demos from product features that can be safely used in real workflows.
Business angle
A lightweight evaluation checklist can reduce wasted budget by showing whether an AI feature improves speed, quality, or decision confidence.
AI PM angle
This is a core AI PM skill: convert vague model potential into measurable product acceptance criteria.
Risk
Without evaluation criteria, teams may ship features that look useful in demos but fail under real user tasks.
Tags and source
Daily file: 2026-07-02
Open original sourceReview metadata
Keep for human review before publication reuse because evaluation terminology should be checked against current product QA practice.