Hardcoded Selectors vs. AI Prompts: A Resilience Benchmark on Etsy

Every developer managing a web scraping pipeline at scale knows the "Monday Morning Breakage." You start your week, check the logs, and realize your primary data source updated its layout at 2:00 AM on Sunday. Your CSS selectors, once surgical and precise, now return None or empty strings that pollute your database. Traditional scraping relies on the structural integrity of HTML. We target specific nodes like div.wt-mb-xs-2 > span.currency-value . But when a site like Etsy—notorious for aggressive A/B testing and dynamic class generation—changes that span to a p , the scraper dies. This article benchmarks the traditional hardcoded selector approach against semantic AI-powered parsing . By simulating a breaking change on an Etsy product page, we can see if LLMs truly "understand" data when structure fails and analyze the real-world costs of both methods. The Contenders: Selectors vs. Semantics Before running the benchmark, we need to define the two philosophies competing for your infras