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.

This second part describes how to integrate LangChain with AWS Bedrock to build AI applications. It covers the implementation of AWS Bedrock with Amazon Titan and Claude models, as well as key LangChain components, including prompt templates, embeddings, memory, and chains. Code examples demonstrate everything from basic model invocation to creating conversational agents with memory, perfect for developers building production AI solutions.

📚 The first part explained the LLM basics of AWS Bedrock.