Better Experiments with LLM Evals — A funnel, not a fork

LLM evals are automated quality assessments that work as a funnel before experiments, not as a replacement.

• •LLM evals measure quality dimensions like relevance, coherence, and tone faster and cheaper than human annotation
• •Evals verify implementation quality while experiments validate real user and business outcomes
• •Running evals before experiments filters non-promising candidates, raising the hit rate of subsequent A/B tests
• •LLM eval scores need continuous calibration against online outcomes to ensure they correlate with actual user value
• •Teams should evaluate judges on A/B test data to diagnose gaps between eval improvements and real user results

This summary was automatically generated by AI based on the original article and may not be fully accurate.