Large Language Models (LLMs) have proven highly effective for web scraping due to their ability to convert unstructured data into structured formats, a task that traditional web scrapers struggle with when faced with changes in web page structures. The flexibility of LLMs allows them to handle dynamic web changes gracefully and efficiently extract significant information while minimizing extraneous content. Key strategies for optimizing web scraping with AI include reducing the context size of AI prompts by filtering unnecessary elements, utilizing LLM functions and structured outputs for reliable data extraction, and providing fallback options to avoid erroneous outputs. Rather than simulating human-like browser interactions, LLMs can navigate websites through links, similar to how search engines index content, thereby avoiding common pitfalls associated with popups, scrolling, and clicking. Platforms like Gumloop offer tools to simplify the implementation of AI-driven web scraping workflows, emphasizing automation without requiring coding expertise.