Build a Three-Layer QA Wall for AI Outputs in 48 Hours

Every AI deliverable you ship without quality checks is a bet against model drift, prompt degradation, and silent failures. This episode builds a three-layer QA wall that catches problems before clients do: rubric-scored LLM judges on every output, weekly golden-set replays to detect drift, and strategic human sampling with red/amber/green thresholds. Based on 2026 research from ICLR AutoMetrics, PLOS One longitudinal studies, and UW Health clinical deployments, you'll learn to scale quality assurance from $50 per evaluation to $0.02 while maintaining client trust. Includes the complete QA Wall Kit with rubric templates, judge prompts, and sampling SOPs you can deploy this week.