Resources/Ebook
When AI Video Breaks: Real Failures and How I Fixed Them
A filmmaker's account of the failures behind generated images, moving performances, and the checks that made the work usable.

A generated shot can look convincing long before it is ready to belong in a film. The face may be wrong, the spoken line may have changed, or the file marked 4K may contain exactly the same number of pixels as its source; each defect asks for a different kind of attention, even when the platform reports the same reassuring word: completed.
I wrote this book for people who have reached that uncomfortable distance between obtaining an image and being able to use it. If you are directing a film, building a character, preparing a series of reels, or making a piece for a brand, these accounts follow the points where my own production decisions met the actual behavior of the tools.
The failures began in different places. Some belonged to the words I wrote, others to the references I attached, and others to the software carrying those instructions to a service; several remained hidden until I inspected the downloaded picture or listened to the finished soundtrack.
Understanding that location changed the repair. A timeout could leave a finished image waiting on the server, while an apparently successful download could retrieve an older version; repeating the generation would have answered neither problem. I learned to compare the request, the stored job, and the resulting file before deciding what needed another attempt.
The order of this book follows that investigation. I begin with rejection and moderation, then examine account control and the conditions of a repeatable request, before tracing the instructions that actually reach the model. From there, I move into identity and references, the visible construction of a shot, and finally the sound, retrieval, and delivery checks that determine whether the work holds together.
You can read the chapters in sequence to follow those connections, or enter through the failure closest to the one in front of you. If a face changes, begin with the identity and reference cases; if a result seems to have vanished, turn to retrieval before paying to create it again.
These are records of particular jobs and tests, with the limits of their evidence kept in view. Some chapters document a correction that worked, while others establish a diagnostic procedure or a check whose later effect was not measured; I keep those distinctions because a useful production habit begins with knowing what the evidence allows me to say. The aim is to make the next decision more deliberate, while keeping the intended film in view.
Keep going

Before You Hit Generate: 20-Point Pre-Flight Checklist for AI Video Prompts
The checks I use to catch avoidable problems while the shot is still a piece of writing.

The Generated Shot Audit: How to Review an AI Clip Frame by Frame
A practical review sheet for finding the moment a generated shot stops making physical sense.

Why Harmless AI Prompts Get Rejected: A Moderation Survival Guide
My working sheet for separating an unclear description from an actual content restriction.