Unlocking the full potential of Large Language Models (LLMs) demands mastery of their essential parameters, which are fundamental components that define how a large language model processes and generates text. The key parameters fall into several categories, including architectural parameters, training parameters, inference parameters, memory and computation-related parameters, and parameters related to output consistency and coherency. Understanding these parameters is crucial because they directly impact model performance, evaluation metrics, and resource utilization. By carefully tuning these core parameters, developers can optimize their LLMs to deliver high-quality outputs efficiently, while managing computational resources effectively. The quality of training data also significantly influences model behavior, making data quality in ML a critical consideration. Galileo's automated hyperparameter optimization tools streamline the process through systematic A/B testing and offline experimentation, enabling developers to explore various parameter configurations to identify the most effective settings without manual trial and error.