AI Feature Quality Evaluation System

Stop guessing whether your AI feature is getting better. Build a systematic quality evaluation program with defined characteristics, simulation sets, and measurable improvement sprints. Includes a 16-field Evaluation Case Log for scoring every simulated interaction against Accuracy, Tone, Hallucination Risk, and 4 other characteristics — plus a Quality Sprint Tracker that records before/after scores so you can show stakeholders concrete evidence of improvement. Built from Ahmed Al-Bahar's quality evaluation process at a Series B SaaS company — designed for PMs and ML engineers who've realized traditional QA doesn't apply to probabilistic AI output. The Kanban Action Board makes triage immediate: escalations are visible, backlog items are prioritized, and hallucinations never slip through undetected. Whether you're running quarterly quality sprints or continuous evaluation cycles, this template gives you the structure to move from anecdotal gut checks to a reproducible evaluation system.