Why AI prompts should split data extraction from decision-making

A software development analysis argues that combining document reading and reasoning into a single AI prompt creates a flawed, untestable system. When an AI is asked to both extract information and make a decision simultaneously, errors in either step are indistinguishable from each other, making debugging nearly impossible. The author proposes splitting such tasks into two stages: a structured extraction phase that captures raw data with source references, followed by a separate reasoning phase that operates on that clean, concise output. This approach makes each stage independently testable, cacheable, and auditable, while also improving overall accuracy by reducing the compounding of read errors and reasoning errors. The structured intermediate output produced between the two stages serves as verifiable evidence, unlike a fused prompt's generated justification, which cannot be reliably traced back to the source document.