Generative AI (Gen AI) is revolutionizing how we interact with computers today, with over 65% of organizations using Gen AI tools to optimize operations. Large Language Models (LLMs) are the backbone of such solutions, enabling machines to produce human-quality text, translate languages, and create different types of content. However, evaluating LLM outputs can be challenging, especially when it comes to ensuring coherence, relevance, and accuracy. This is where the concept of LLM-as-a-judge emerges as a solution to address these challenges. The framework uses one LLM to evaluate the output of another - AI scrutinizing AI. Studies suggest that LLM judgments match about 80% of human evaluations, indicating that two LLMs agree on judgments at the same rate as human experts. This scalable and explainable method is a valuable alternative to hiring human judges. LLM-as-a-judge can be used to augment human reviews, improve text data quality for LLM, and enhance AI alignment. However, it also presents challenges such as data quality concerns, inconsistency in complex evaluations, and potential biases inherited from training data. Tools like Encord can help address these issues by providing advanced features for text annotation, reinforcement learning from human feedback, and model-assisted labeling. By leveraging LLM-as-a-judge and tools like Encord, organizations can create scalable and cost-effective solutions for evaluating AI systems while ensuring reliability and fairness in their judgments.