The Five Dimensions of AI Video Prompting

A practical framework for Kling, Seedance, and Runway — from someone who’s actually used them.

Video generation is not text generation. Most people treat these tools the way they treat ChatGPT: throw in a long, detailed paragraph and hope the model figures it out. That’s exactly backwards.

Think of your prompt as a sculptor’s chisel on marble. Every word you add is another strike. The more you write, the harder it becomes for the model to carve something coherent. This is why the industry consensus — among people actually producing award-winning work — is to never let AI write your prompts. Keep them short. Keep them precise. Every word earns its place or gets cut.

The best prompts I’ve seen cover five dimensions and nothing more.

1. Subject and Motion

Your primary character, any secondary characters, and their movement trajectories. Not what they look like in granular detail — the model can see your reference image. Just who’s moving, and where.

2. Environment and Mood

Weather, time of day, emotional atmosphere. This is where you set the tone. A sunset behind rain-streaked glass tells a different story than noon in a desert. One line is usually enough.

3. Optics and Camera

Lens angle, lens type, and camera movement. This is the dimension most people skip, and it’s the one that separates amateur output from cinematic output. Learn the film industry terminology: dolly-in, rack focus, crane shot, Dutch angle, whip pan. If you don’t know what these mean, stop prompting and go study them first. The camera is half the storytelling.

4. Timeline and Pacing

How the scene evolves across time. State changes, rhythm, acceleration. Does the character start still and then move? Does the rain intensify? Does the camera slow down as the subject turns? This dimension controls the emotional arc within your 3–5 second clip.

5. Aesthetics and Rendering

Art style, resolution, frame rate, and render settings. This is your bottom layer — the visual grammar underneath everything else. Specify it once and let it anchor the rest.

The Rule Most People Break

If your reference image already contains specific lighting, colour grading, or character details — do not repeat them in your prompt. The model can see the image. Restating what’s already there creates conflict, not reinforcement. Your prompt should only add what the image doesn’t show: motion, camera, and time.

Which Model, When

No single model does everything well. The professionals I’ve spoken to and the work I’ve done myself consistently point to a multi-model workflow.

Kling 3.0 is strongest for cinematic quality — natural dialogue, subtle facial expression shifts, emotional close-ups. If you need a character to feel something, Kling handles it best. Its weakness is large-scale scenes and fine environmental detail.

Seedance 2.0 excels at detail fidelity and animation-style work. Complex scenes with many moving elements, architectural environments, and precise motion choreography. It’s the workhorse for anything that needs spatial consistency across a longer sequence.

Runway remains competitive for certain stylistic applications, but the gap has narrowed. It’s often most useful as a secondary pass or for specific aesthetic effects.

The current professional workflow looks like this: use a tool like Banana Pro to extract 25 coherent keyframes from your concept, composite those frames into a single reference sheet, and feed that sheet into Seedance for generation. This gives the model a visual storyboard to follow rather than relying purely on text interpretation.

The key point: you should be mixing two to three models per project. Kling for the close-ups, Seedance for the wide shots, a third for transitions or effects. Treating any single model as your only tool is the fastest way to hit a quality ceiling.

What Award-Winning AI Films Actually Cost

There’s a misconception that AI video is free or nearly free. The tools are cheap. The process is not.

A competition-grade AI short film — the kind winning festivals or landing commercial contracts — typically requires one to two months of planning and production. The direct costs run between $1,000 and $2,000 USD, covering compute credits, reference asset creation, and post-production. That doesn’t count the hours of iteration, prompt refinement, and model-switching that go into getting a final cut right.

This is not “type a sentence and get a movie.” It’s a production discipline with its own craft, its own vocabulary, and its own economics. The people producing exceptional work treat it that way.

David Leung is a compliance professional and AI practitioner based in Hong Kong. He writes about the tools he actually uses — and the ones he’s stopped using.