To deploy production grade GenAI systems, large language models must be evaluated for reliability, output consistency, and system stability, not just theoretical benchmark performance. When these models are integrated into production environments, engineers face complex challenges such as data heterogeneity, hallucinations, and undefined failure modes. This talk examines the technical difficulties of measuring model fidelity in live environments and presents architectural approaches that focus on statistical sampling, rigorous ground-truth validation, and real-time observability to ensure metrics accurately reflect functional utility and system integrity.
Ponentes: Misam Abbas