Web scraping often faces challenges due to dynamic website layouts and stringent anti-bot protections, but using Meta's LLaMA 3, an AI-powered language model, offers a more resilient approach by extracting data contextually. Released in April 2024, LLaMA 3, with versions up to 405B parameters, improves data extraction by mimicking human-like understanding, making it suitable for complex sites like Amazon. The guide outlines a detailed process for setting up a Python-based scraper using the lightweight tool Ollama to run LLaMA models locally. It employs a multi-stage workflow involving browser automation, HTML extraction, Markdown conversion, and LLM processing to output structured data in JSON format. Despite the advanced capabilities of LLaMA, overcoming anti-bot measures remains a challenge, for which solutions like Bright Data's Scraping Browser are recommended to handle CAPTCHA challenges and dynamic content seamlessly. The guide also suggests further enhancements like multi-page support and secure credential management to improve the scraper’s robustness and efficiency.