AI judge models, such as reward and critic models, are transforming the landscape of AI evaluation by providing rigorous assessments that enhance the training and deployment of AI systems. Reward models offer numerical scores to optimize AI behavior using Reinforcement Learning from Human Feedback, while critic models provide detailed feedback to address specific errors, enhancing AI reliability in production environments. These models are integral to improving AI systems by generating multiple responses, selecting the best options, and refining outputs through revision loops and data filtering. However, challenges such as inconsistent scoring and the limitations of relying solely on benchmarks highlight the need for consistent, interpretable scoring methods and custom constraints tailored to specific business requirements. As AI judge models continue to evolve, they promise to streamline training and improve performance across AI applications, making them essential tools for AI developers aiming for quality and reliability in their systems.