How large organizations and enetrrpises standardize LLM benchmarks

As large language models (LLMs) transition from experimental projects to production systems in organizations, there is a growing need to establish a consistent and meaningful evaluation framework that goes beyond simple benchmarking. Standardizing LLM evaluations involves creating a comprehensive protocol that includes public comparability through academic benchmarks, internal golden sets for real-world tasks, continuous regression testing, and risk and safety assessments. Organizations must carefully document the evaluation process, incorporating elements like task specifications, prompting rules, and scoring mechanisms, while ensuring evaluations are auditable and aligned with external standards such as the NIST AI Risk Management Framework. By integrating evaluation into continuous integration pipelines and treating it with the same rigor as software testing, organizations can maintain control over model performance and reliability. However, pitfalls such as data contamination, benchmark leakage, and overfitting to proxy metrics can undermine credibility, necessitating measures like private evaluation sets and human evaluation with strict rater calibration. Ultimately, the goal is to create a robust evaluation system that functions as a control loop, enabling organizations to confidently deploy LLMs by catching potential failures before they impact users.