The Input Shapes the Output: Why AI Notetaking Architecture Matters More Than Accuracy

This article reveals that choosing the 'best' AI notetaker depends not on accuracy but on architectural philosophy. Otter and Granola represent fundamentally opposed models—one captures everything verbatim, the other uses your typed fragments to guide augmentation. The deeper insight generalizes across all LLM design: what you feed the system (human judgment vs. raw data) determines what emerges, reshaping how we should evaluate any AI tool.