This blog series focuses on improving the reliability of Large Language Models (LLMs) used as judges, which are AI systems that evaluate human responses. To make these LLMs more reliable, it's essential to address common biases and limitations, such as nepotism bias, verbosity, and attention bias. The authors propose several practical strategies to improve the performance of LLM judges, including using assessments from multiple models, extracting relevant notes, running multiple passes, and applying Chain-of-Thought style reasoning. By implementing these strategies, developers can work towards creating more accurate, fair, and reliable evaluations across various tasks and domains.