Generative AI models are increasingly used in various fields, necessitating robust evaluation processes to ensure their effective deployment in real-world applications. Traditional evaluation methods, which often rely on binary success/fail metrics or comparisons against a golden source, are inadequate for the nuanced demands of modern AI. Modern approaches, such as rubric-based evaluations, offer a structured and multi-dimensional framework that includes subjective criteria like friendliness and empathy, allowing for deeper insights and faster iteration. These evaluations are crucial for optimizing AI models across dimensions such as quality, cost, latency, and safety, and involve both human and programmatic assessments to ensure comprehensive model analysis. Implementing these evaluations is an iterative process, starting with simple cases and expanding to more complex scenarios, which helps businesses make informed deployment decisions. As AI technology advances, the importance of such thorough evaluation frameworks will continue to grow, fostering innovation and trust in AI solutions.