Data collection pipelines often incorrectly measure success based solely on HTTP status codes, leading to false positives when a site returns a "200 OK" response with no data or a modified page. This is a significant issue for AI/ML practitioners who rely on scraped data for training and analysis, as it can mask critical changes in data sources and introduce errors into downstream processes. To address this, a more robust approach involves asserting the shape and content of the payload itself, rather than simply trusting the transport layer.
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