So far, you have already learned about the capabilities of foundation models on Amazon Bedrock. Now, in this chapter, you will explore the customization of models on Amazon Bedrock. The objective is to meet specific business requirements and integrate domain knowledge, leveraging customization of models. The chapter begins with an explanation of fine-tuning. It uses a fictional company as an example. This company is called AnyFintech. It encountered problems with its generative AI chatbot. The chatbot gave responses that were too generic and not relevant. These responses did not match the company's specific guidelines. You will understand the importance of fine-tuning in generative AI. Fine-tuning is crucial for enhancing performance. It also increases relevance in specialized applications. The chapter explores essential terms related to fine-tuning. You will get an overview of parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), and hyperparameters. It discusses the reasons for the necessity of fine-tuning. The chapter compares different approaches. These include in-context learning, full training, and continuous pre-training. The chapter introduces continuous pre-training. It uses a fictional marketing analysis firm as an example. This firm is called AnyMarketingAnalyst. The firm also faced issues with its AI chatbot. The chatbot provided outdated responses. It also gave generic responses. You will learn about the significance of continuous pre-training. You will also learn strategies for deciding between fine-tuning and continuous pre-training. There will be a comparison between retrieval-augmented generation (RAG) and model customization. The chapter concludes with insights into monitoring and governance practices to ensure the effectiveness and compliance of customized AI models.