Testing the Untestable: What 28 Practitioners Told Us About QA for AI
AI outputs shift, ground truth is scarce, and most teams are testing on instinct. This field report captures how 28 practitioners handle it — the challenges, the tooling gap, and what they check before trusting a model in production.
Inside: five findings on where AI testing actually breaks, a practical techniques box you can apply this week, and honest input from people building and implementing AI products day to day.
What you get:
- Five field-tested findings on why AI breaks differently from software;
- Techniques worth stealing — six proven tactics you can run without any framework;
- The trust-first checklist practitioners use before a model reaches production;
- A clear read on the framework gap — who builds their own tooling and why;
- The guardrail and reasoning tests that catch the right answers reached the wrong way;
- What “testing AI” even means — so stakeholders know where to trust a model.