OpenAI's article introduces evaluation frameworks, or "evals," designed to help businesses maximize the effectiveness of AI systems. With over a million businesses already using AI, many still struggle to achieve desired outcomes. Evals provide a structured approach to transform vague objectives into measurable results, ensuring that AI tools meet specific business needs. By defining clear goals, conducting thorough error analysis, and creating a "golden set" of examples, organizations can better align AI performance with expectations. The article distinguishes between two types of evals: frontier evals and contextual evals. Frontier evals assess model performance across various domains, while contextual evals focus on specific workflows relevant to individual organizations. Business leaders are encouraged to develop these contextual evals tailored to their unique environments. The process involves a cross-functional team that includes both technical and domain experts to define success metrics and outline workflows, ensuring that all perspectives are considered. Measuring performance is the next critical step. This involves creating test environments that mimic real-world conditions to identify how AI systems fail. The article emphasizes the importance of using realistic examples and edge cases during testing, along with traditional business metrics or new, tailored metrics. Finally, continuous improvement is essential, requiring regular audits and adjustments based on the insights gained from evals. By iterating on these processes, businesses can enhance their AI systems and better achieve their objectives.