GenAI Quality Evaluation Plan

Shipping a GenAI feature is the beginning, not the end. Most teams track accuracy metrics at launch and move on — which means quality drift goes unnoticed until customers complain. Without a structured evaluation framework, 'it seems to be working' is the best answer anyone can give. This template gives you three things: a Characteristics Registry to define what 'good' looks like for each feature, an Evaluation Log to record quarterly results by characteristic and severity, and a Quality Sprint Tracker to manage fixes through a dedicated improvement pipeline. Run the built-in 7-step task once per quarter per feature. It walks you from sampling outputs to scoring characteristics, logging results, queuing sprints, and generating the leadership readout. The methodology is grounded in probabilistic output evaluation — designed for teams that understand human review is unavoidable and want a credible framework for running it. Built for AI PMs and Technical PMs who own GenAI features post-launch and take quality seriously enough to measure it systematically.