Web scraping, a method for extracting data from websites, is pivotal for acquiring large datasets necessary for fine-tuning large language models (LLMs) to generate domain-specific responses. This article explores web scraping using Python, detailing tools like Beautiful Soup—a library for parsing HTML and XML to extract data from static web pages—and Selenium, which automates browser interactions to gather data from dynamically loaded content. The tutorial guides readers through creating scraping scripts for both static and dynamic websites, emphasizing their applications in enhancing LLMs by ensuring the models are trained on the most relevant and up-to-date information. By demonstrating the scraping of a sandbox bookstore and the FreeCodeCamp YouTube channel, it illustrates how each tool can be harnessed for specific data extraction needs, thereby aiding in targeted dataset compilation for AI model refinement and other data-driven projects.