The note
Across conversational AI projects, the recurring failure mode is the same: behavior drifts silently. A model upgrade, a reworded prompt, or a new intent quietly regresses answers that used to work — and nobody notices until it reaches production.
The fix is not more prompt tinkering. It is testing discipline borrowed from software engineering: repeatable scenario suites that exercise real conversations, baseline comparisons that catch behavioral drift, and evaluation scorecards with human review loops on the edge cases. This is the same approach behind the conversational quality engineering work described in my technical proof of work — regression suites and drift checks that raise release confidence and cut production regressions.
These were also the themes I brought to the European Chatbot & Conversational AI Summit 2026: as the field matures, rigorous testing is shifting from a nice-to-have to the core differentiator between conversational systems that scale and ones that stall.