CONCEPT
Output Interrogation
The AI-era practice of reading generated output against the grain — treating it as a hypothesis requiring verification rather than a finished product requiring consumption.
Output interrogation is the disciplined practice of reading AI-generated output with active critical engagement — treating each output not as a finished artifact to be consumed but as a provisional hypothesis to be tested. The practice integrates the Renaissance ars critica with the specific demands of AI-era content: distrust of fluency, attention to invisible omissions, independent verification of connections, and deliberate resistance to the smooth surface that induces premature acceptance. Ann Blair's framework treats output interrogation as one of the four core operations of AI curatorial practice (alongside prompting, selecting, and integrating), and as a specific skill that can be taught, refined, and institutionalized.
In The You On AI Field Guide
The practice begins with the recognition that AI output is provisional by nature. Every output is a draft for evaluation, not a finished product for acceptance. This disposition must be maintained across many iterations per hour, for hours per day — a sustained effort that constitutes the specific labor AI collaboration demands.
The operations of