We've learned to build and host large language models by tweaking parameters such as temperature and frequency penalty to control the model's output, using techniques like cycle detection, sampling, layering another model, training to break loops, and finetuning on user feedback to filter out nonsense. We also use benchmark datasets and A/B testing to evaluate performance and make improvements. To speed up training and inference, we've adopted faster transformer architectures, knowledge distillation, and quantization techniques. Effective prompt engineering is crucial, as prompts change with context and can be generated by other models. Deploying the model on GPUs in the cloud requires careful consideration of resource utilization and monitoring to ensure optimal performance and user experience. By tracking system-level metrics and storing user feedback data, we fine-tune our models for better quality and consistency.