This comprehensive guide explores fine-tuning Large Language Models (LLMs) on AWS SageMaker, covering essential concepts from basic language model architecture to practical deployment. Learn about transformer-based models, self-attention mechanisms, and positional encoding that enable parallel processing of sequential data. Discover Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA that reduce trainable parameters by up to 10,000 times while maintaining performance. The tutorial demonstrates hands-on implementation using Databricks Dolly-v2-3b model, including dataset preparation, tokenization, and training configuration. Explore prompt engineering strategies including zero-shot, few-shot, and chain-of-thought approaches for optimal model outputs. Master n-gram tokenization principles underlying modern BPE and WordPiece methods. Follow step-by-step deployment instructions using SageMaker’s DJL framework, from model artifact creation to inference endpoint testing. Perfect for AI practitioners seeking cost-effective, domain-specific LLM customization on AWS infrastructure.