7 Habits of Highly Effective Generative AI Evaluations - Justin Muller
Jun 3, 2025 · 25:39
Principal Applied AI Architect Justin Muller argues that generative AI evaluations are the missing piece to scaling, offering seven habits from over 100 projects. He recounts a customer whose document processing workload had 22% accuracy with no evals; after building an evaluation framework, accuracy reached 92% and the system became the largest such workload on AWS in North America. The habits include fast 30-second eval cycles using AI-as-judge, quantifiable scores averaged across numerous test cases, explainable reasoning for both generation and scoring, segmented evaluation through prompt decomposition, diverse test sets covering all use cases, and traditional techniques for numeric outputs or cost/latency. He emphasizes that evaluations should primarily discover errors, not just measure quality, and that prompt decomposition into chained steps often boosts accuracy by removing dead space.
