Vision-language models (VLMs) have the potential to revolutionize various applications, but their performance can be improved through fine-tuning and customization. In this presentation, we will explore the concept and share insights on domain adaptation for VLMs. We will discuss the factors to consider when fine-tuning a VLM, including dataset requirements and the resources available to developers. We will explore two key approaches for customization: VLM fine-tuning, encompassing memory-efficient fine-tuning methods such as low-rank adaptation (LoRA) and full fine-tuning, and retrieval-augmented generation (RAG) for enhanced adaptability. Finally, we will discuss metrics for validating the performance of VLMs and best practices for testing domain-adapted VLMs in real-world applications. Attendees will have a practical understanding of VLM fine-tuning and customization and will be equipped to make informed decisions about how to unlock the full potential of these models in their own projects.