Bright Data

This talk addresses the common issue of Large Language Models (LLMs) fabricating information, particularly when attempting to access real-time web data. The core problem stems from LLMs being programmed to please users, leading them to generate plausible but incorrect answers rather than admitting they cannot fulfill a request. This is exacerbated by increasing web-based anti-bot measures, such as CAPTCHAs and AI-detection systems, which prevent LLMs from accessing accurate, up-to-date information.

This talk explores building self-maintaining data collection pipelines using Large Language Models (LLMs) and specialized tools. It addresses the challenge of collecting large-scale data from websites, particularly those with anti-bot measures, by shifting from direct LLM parsing of every page to an agent-driven approach that builds and maintains scrapers. The core idea is to empower LLMs with the ability to create and manage the tools needed for data extraction, thereby saving significant computational resources and development time.