The guide explores how leading organizations are mastering functional correctness to build AI systems that deliver reliable, consistent results in real-world applications. Functional correctness represents the fundamental requirement that AI systems behave exactly as specified, not just in controlled environments but also in the complex, unpredictable world of production deployments. Ensuring accurate functional correctness in AI systems poses unique challenges that distinguish it from traditional software testing. The emphasis on functional correctness leads to tangible improvements in production environments, such as enhanced decision-making quality, reduced operational risks, and increased stakeholder trust. To achieve this, organizations must navigate complex trade-offs between competing demands for effective AI risk management. This includes balancing consistency and creativity in LLMs, adapting systems to new patterns and information while maintaining reliable performance, and ensuring compliance with regulatory requirements regarding data privacy, fairness, and transparency. Galileo's evaluation and optimization framework offers domain-adaptive evaluation capabilities with flexible metrics and evaluation criteria tailored to industry-specific benchmarks, tackling challenges such as response drift, where AI system outputs gradually deviate from expected behavior over time. By understanding functional correctness in AI and leveraging innovative solutions like Galileo, enterprises can ensure their AI systems operate as expected, aligning decision-making processes with predefined business objectives.