An LLM-as-Judge Won't Save The Product—Fixing Your Process Will
Eugene Yan
A product evaluation approach for AI systems requires following the scientific method through iterative cycles of observation, annotation, hypothesis testing, and experimentation rather than relying solely on automated LLM-as-judge tools. The process involves building a 50:50 split of passing and failing annotated samples across the input distribution, designing controlled experiments with clear success metrics, and measuring quantifiable improvements like accuracy gains or defect reduction. Proper eval-driven development integrated with continuous human oversight of outputs enables teams to systematically improve AI products through objective feedback loops instead of intuition-based assessments.
Why it matters
Applying the scientific method, building via eval-driven development, and monitoring AI output.
