In the context of financial AI systems, traditional chatbot evaluation metrics fall short when addressing the stringent requirements of regulatory bodies like the Consumer Financial Protection Bureau (CFPB). Unlike e-commerce chatbots, errors in financial AI can lead to severe legal and compliance issues, necessitating a shift from optional optimization to mandatory compliance infrastructure. The guide emphasizes creating a robust framework that aligns technical excellence with regulatory standards from the outset, focusing on measurable outcomes such as factual accuracy, policy consistency, and harm prevention. It advocates for a comprehensive compliance matrix that maps regulations to specific accuracy thresholds, using domain-specific datasets and real-world testing environments to ensure performance under operational stress. The guide also highlights the importance of documenting decision-making processes to satisfy regulatory scrutiny, implementing risk assessment and safety protocols to prevent unauthorized advice, and balancing compliance with customer experience quality. Continuous monitoring and improvement systems are crucial to adapt to changing market conditions, regulatory updates, and customer behavior, while participation in industry consortia and third-party audits helps maintain current benchmarks. This systematic approach, supported by tools like Galileo, aims to transform compliance from a reactive burden into proactive protection, ensuring financial AI systems meet regulatory demands while delivering excellent customer experiences.