Document processing pipelines, which extract data from files like PDFs, often suffer from subtle failures that go unnoticed until they impact users. This focus on "silent" errors—where data is skipped or incomplete—is a critical reliability concern for teams building AI-powered applications that rely on this data. A key takeaway is the need to design systems that explicitly categorize failure states and surface clear, human-readable explanations for those failures, ensuring data consumers understand limitations and can trust results.
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