Deploying updates to language models carries inherent risk, making <a href="https://npprteam.shop/en/articles/ai/evaluating-the-quality-of-llm-systems-test-sets-regressions-ab-testing/" / rel="nofollow ugc">LLM regression testing best practices for production</a> a critical operational discipline. When you push new model versions or fine-tuning adjustments, undetected regressions can degrade user experience, damage customer trust, and create compliance issues before they're caught. This resource walks through detecting performance degradation across multiple dimensions—accuracy, latency, output consistency, and edge case handling—ensuring changes don't introduce silent failures in live systems. The methodology covers automation strategies, test coverage optimization, and thresholds for triggering rollbacks when quality drops below acceptable bounds. Teams managing LLM systems in healthcare, finance, or customer-facing applications especially benefit from these guardrails, as regressions in sensitive contexts carry real consequences.