Bug Bashes for AI Features: Sampling a Distribution, Not Hunting Defects
The classic bug bash is a deterministic ritual built for deterministic software. Ten engineers crowd a Slack channel for two hours, hammer a checklist of golden-path flows, and file tickets with crisp repro steps: "Click X, see Y, expected Z." It works because the system under test is reproducible — same input, same output, same bug, every time.
Run that exact ritual against an AI feature and you will produce two hundred tickets, close one hundred and eighty as "expected stochastic variation," and miss the twenty that signal a real cohort regression. The format isn't just stale; it's actively miscalibrated. A bug bash against an LLM-backed feature is not a defect-hunting session. It is a sampling exercise against a probability distribution, and the team that runs it like a deterministic test session is collecting noise and calling it signal.
This post is about how to redesign the bug bash for stochastic systems — what to change about the format, the participants, the triage rubric, and what counts as "done."
