In this chapter, you will learn about evaluating generative AI models leveraging Amazon Bedrock model evaluation functionality. This is very essential to build enterprise-grade products. Evaluating these models helps ensure reliability. It also ensures safety. Additionally, it ensures effectiveness. Generative AI models have unique challenges. One major challenge is hallucinations. Another challenge is bias. These models must also be coherent like humans. This requires special evaluation techniques. You will see real-world examples. For instance, John faced issues with text moderation. Emily had challenges in image generation. These examples show the consequences of poor model evaluation. The chapter explores the lifecycle of model evaluation. It covers pre-training assessment and deployment monitoring. Key aspects include bias and fairness audits. Human review is also important. Reinforcement learning with human feedback (RLHF) will also be discussed.