LLM-as-a-Judge is emerging as a vital tool for evaluating outputs generated by large language models (LLMs) due to its scalability and consistency compared to traditional human reviews. This approach involves using one LLM to assess the outputs of another, employing various techniques such as pairwise comparison, single answer grading, and reference-guided scoring. Although it offers advantages like cost-efficiency and generalizability, LLM-as-a-Judge also faces challenges, including prompt dependency, biases, and reproducibility issues. It is particularly useful for tasks involving open-ended outputs where exact matches are not feasible, and by adjusting prompts, it can evaluate various criteria like tone and factual accuracy. To address its limitations, strategies such as fine-tuning custom LLMs, mitigating biases, and developing secure prompt designs are being explored. The concept is gaining momentum, with research focusing on handling adversarial attacks and creating personalized judgment systems that reflect diverse user values.