# Better Experiments with LLM Evaluations Synergy between AI assessments and A/B testing - **Source:** [Spotify Engineering](https://engineering.atspotify.com) - **Published:** 2026-05-18 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/548ded4c-02e4-4f6c-96f7-cfe45bf4dbe8 ## The Power of LLM Evaluations Automated LLM evaluators complement traditional A/B tests by assessing content quality at scale. At Spotify, only 12% of tests yield positive results while 64% provide learnings. This new approach is faster and cheaper than human evaluation. ## Verification vs Validation Evaluations verify technical quality (verification) while A/B tests validate real user response (validation). This dual system helps filter failed ideas early and preserves test bandwidth. ## Hypothesis Generation Mechanism LLM evaluators uncover unexpected patterns to generate new product improvement ideas. For example, a trust violation detection system identifies unknown issues, enabling proactive solutions. ## Limitations and Risk Management Evaluations cannot predict long-term user behavior. 42% of launched experiments at Spotify are rolled back due to secondary metric regressions. Systematic monitoring with guardrail metrics is essential. ## Dual-Layer Calibration LLM evaluations add a second validation layer atop traditional quantitative metrics. Both layers require continuous calibration with real user outcomes. As seen with Anthropic's Opus 4.5, evaluation results can sometimes be misleading. ## Continuous Improvement Loop Mutual feedback between evaluation and experiment data makes the system smarter over time. Evaluation results misaligned with user responses provide critical calibration signals. This iterative process strengthens as AI advances. ## Sources covering this story - https://engineering.atspotify.com/2026/5/better-experiments-with-llm-evals-a-funnel-not-a-fork/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._