How to Build a Repeatable Test Harness for Evaluating AI Video Generators

AI video demos often showcase polished final clips while hiding the failed attempts, credit waste, and multiple retries that occur behind the scenes. A structured evaluation framework treats AI video generation as a controlled experiment rather than a prompt-writing exercise. The approach breaks work into single, well-defined shots, each containing one subject, one action, one camera behavior, and a clear list of elements to preserve or avoid. Prompts are compiled from structured data objects, making it easier to isolate and debug specific failures such as identity drift, broken geometry, or unwanted camera motion. This tool-agnostic workflow helps teams assess motion accuracy, output stability, and realistic generation costs before committing significant resources.