Why AI Engineers Should Treat Data Chunking as a Core Design Decision

A software engineer published a detailed post-mortem after their AI system failed catastrophically when tested against a 4,000-plus chapter web novel. The root cause was treating text chunking — splitting large inputs into smaller pieces — as a last-minute fix at the prompt level, rather than a foundational architectural decision. As a result, every other layer of the system, including the database, job queue, API, and frontend, had silently assumed the entire document would always fit as one unit. The engineer introduces two principles — 'chunk-first' and 'chunk-native' — arguing that assigning chunks their own identity in the schema early on keeps the system flexible and avoids costly refactors later. The post-mortem cost the author over a month of rework and was written to help other developers avoid the same mistake.