Prompt Structure for AI Video: A Practical Framework

Most disappointing AI video outputs are not model failures. They are prompt failures caused by packing five ideas into one sentence and hoping the model sorts out priority. After a few hundred generations, a simple ordering discipline turns out to matter more than vocabulary.

Why order beats wording

Diffusion-based video models resolve a prompt roughly in the order attention is distributed: subject identity first, then action, then framing, then atmosphere. If the atmosphere clause arrives before the subject, the model will happily invent a subject that fits the mood. Reordering the same words often fixes an output that looked unrecoverable.

The five-slot template

I now write every prompt as five slots, in this order:

1. Subject — one noun phrase, concrete, with the two visual traits that must survive. “A weathered brass alarm clock, dented on the left edge” beats “an old clock”.
2. Action — a single continuous verb. Two actions in one shot produce a morph, not a cut.
3. Camera — position and movement: slow push in, locked-off wide, handheld follow. Naming a movement without naming the starting distance is the most common source of unusable framing.
4. Light — direction plus quality. “Low side light, hard shadows” gives the model something to render; “cinematic lighting” gives it a mood word it cannot resolve.
5. Duration and pacing — how much of the shot the action should occupy. A four-second clip cannot hold a three-beat story.

Written this way, a prompt reads like a shot list rather than an advertisement. That is the point.

Negative space and motion budget

Two constraints are worth stating explicitly. First, motion budget: the more the subject moves, the less detail the model can hold on its surface, so product shots should keep subject motion small and let the camera do the work. Second, negative space: leaving a third of the frame empty gives the model a place to make mistakes that nobody notices.

A prompt is not a wish list. It is a constraint set that narrows the space of plausible frames.

Iterating without restarting

When a generation misses, change one slot at a time and keep a note of what changed. Re-rolling the same prompt and hoping is expensive; changing camera distance by one step usually tells you whether the problem was framing or subject definition. Most of my working prompts reached their final form after four or five single-variable edits, not after twenty fresh attempts.

Tooling notes

I run these tests in Kling 3.0 AI Video Generator, which exposes prompt, camera direction and duration as separate controls — that separation is what makes single-variable iteration practical. The same framework transfers to other text-to-video models, though the sensitivity of each slot differs: some weight camera language heavily and ignore light entirely.

Checklist

• One subject, two defining traits.
• One action, one verb.
• Camera position before camera movement.
• Light direction plus quality.
• Duration matched to the number of beats.
• One change per iteration.

None of this is model-specific magic. It is the same discipline a storyboard artist applies before a single frame is drawn, and it survives every version bump because it describes the shot rather than the software.