Crawling
An automation technology where bots navigate the internet, discover web pages, and collect information. Beyond simple data extraction, it is a core process that serves as the foundation for search engine indexing or building large-scale datasets for AI model training.
Detailed explanation
Why it matters in tool selection
While traditional crawlers focused on static text, 'JavaScript rendering capability' and 'semantic analysis' functions are key when selecting modern AI tools. Choosing tools that include features allowing AI to understand the content and convert it into an easily processable format—beyond simple collection—will reduce data refining costs.
Checklist for operating AI crawlers
- Compliance with robots.txt and the latest protocols (llms.txt)
- Support for JavaScript execution and dynamic content rendering
- Proxy rotation and rate limiting features to prevent IP blocking
- Integration capability with real-time RAG (Retrieval-Augmented Generation) pipelines for the collected data
Representative cases
1. Search Engines: Googlebot visits and indexes billions of pages daily. 2. AI Training: Common Crawl crawls the entire web to provide training datasets for models like GPT-4. 3. Real-time AI Assistants: Crawling and summarizing recent news sites to answer user queries.
Commonly confused terms
Crawling
The act of 'visiting' web pages to collect the overall structure and links (URL-focused)
Scraping
The act of extracting only the 'necessary data (prices, titles, etc.)' from a collected specific page (data-focused)