With the increasing adoption of generative AI models in modern applications, robust evaluation is essential to guarantee their reliability, fairness, and effectiveness. Evaluating complex generative models presents significant challenges due to the complexity and variability of outputs. To address these challenges, innovative evaluation metrics are being developed, including automated metrics such as BLEU, ROUGE, or perplexity, which provide quantifiable assessments. However, these metrics often fail to capture nuances like contextual relevance or subtle biases. Advanced tools like Galileo bridge this gap by offering deeper insights into model performance, beyond standard quantitative measures. Platforms like Galileo and EvalAI facilitate the integration of automated metrics with expert judgments, ensuring AI solutions align with technical standards and user expectations. Qualitative evaluation methods provide deeper insights into AI's effectiveness and trustworthiness through human judgment, expert review, and user experiences. Addressing fairness and bias is crucial in training data, including diverse perspectives and conducting bias audits to maintain fairness and compliance. As AI technologies evolve, so do evaluation methods, focusing on improved techniques, ethical considerations, and automation.