I Tested 15 LLMs for Web Scraping and Built Heuristics Instead

Testing 15 large language models (LLMs) for web scraping revealed that their performance is hindered by the massive input size of typical web pages, leading to high latency and cost. Developers found success by creating a heuristic-based pre-processor that significantly reduces page size, making LLMs more efficient for labeling fields rather than detecting patterns. This approach optimizes AI usage in real-world applications, enhancing both speed and accuracy.

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