The source outlines the process and benefits of fine-tuning custom embedding models, particularly for improving Retrieval-Augmented Generation (RAG) systems. It explains why and when such fine-tuning is advantageous, often addressing the limitations of general-purpose models in specialized domains. The text details key considerations for fine-tuning, including computational requirements, selecting a base model, preparing datasets, and evaluating performance. Finally, it provides practical methods for integrating a fine-tuned model with a Weaviate vector database using either Hugging Face or Amazon SageMaker modules.