Databricks' Senior Director of Product for AI has extensive hands-on experience with generative AI models, emphasizing the importance of focusing on safety, accuracy, and governance to ensure reliable and ethical solutions. To evaluate complex generative tasks, teams are adapting metrics to specific questions or scenarios, using model-in-the-loop approaches and human-in-the-loop methods when needed. Governance is crucial, requiring a structured, dynamic, and ongoing approach that involves monitoring, evaluation, and adjustment across the organization. Evaluation of GenAI systems requires detailed investigations into system outputs, asking whether they're correct, fulfill the expected outcome, and are optimal for the intended use. Continuous iteration is essential, involving rigorous data-driven approaches to improve performance and accuracy, such as creating robust datasets, fine-tuning prompts, and generating synthetic data. Effective GenAI solutions require integrated systems spanning foundation models, context data, training data, embedding models, vector databases, observability, and more, each working together in sophisticated multi-step processes that demand thoughtful system design and ongoing monitoring.