LLM evaluation is essential for ensuring the quality and safety of AI systems, yet it faces challenges in achieving scalable, accurate, and cost-effective assessments. CROWDLAB, an open-source software developed by Cleanlab, addresses these challenges by using statistical techniques to improve the accuracy of labels generated by both human and AI annotators. It enhances the evaluation process for language models by efficiently combining human input with AI-generated probabilistic classifications to produce consensus ratings while identifying unreliable reviewers. The software has been successfully applied to the MT-Bench dataset, where it determines consensus ratings and highlights areas for further review. CROWDLAB works best with effective machine learning models, which Cleanlab Studio provides to ensure confidence in LLM evaluations.