Midjourney rewards you for piling on adjectives. Video models punish you for it.
If you’ve used Kling, Runway Gen-3, or Luma, you’ve probably watched a camera spin while a character’s face melts into their collar. A Reddit user on r/PromptEngineering broke down why this happens and shared a prompt structure that fixed it for them. It’s worth borrowing.
The key idea
A video model doesn’t read your prompt as a description of one image. It reads it as a list of movements to carry out. Every motion word you add is another job for the model. It also has to keep every frame consistent with the one before it, so each extra movement multiplies the chances that something drifts.
Take this prompt:
“Cinematic drone shot of a car speeding through a mountain curve at sunset, camera rotates around the car, fast motion, 4k”
That’s four movements at once: the car’s speed, the road curve, the drone’s forward flight, and the orbit. The model tries to do all of them, they fight each other, and you get a car that warps or wheels that slide sideways. The author calls this motion conflict.
The old way vs. the new way
The old way is the image-prompt habit: stack 20 descriptive words and hope the model sorts it out. That works fine for stills, because there’s nothing to resolve over time. Words like “cinematic,” “dynamic,” and “fast” are harmless in an image. In a video they quietly turn into extra motion.
The new way is a strict 4-part shot syntax. Each part gets one job:
- 🎬 Subject and starting state. Describe one still frame, like a snapshot of where the shot begins.
- 🏃 Primary motion. Exactly one action verb.
- 🎥 Camera direction. One camera axis only.
- 💡 Lighting and environment. This grounds the world so the model has something stable to hold onto.
The author says this took them to consistent 8 out of 10 shots. That’s one person’s experience, not a benchmark, but the logic holds up. You are no longer asking the model to invent a plan. You are handing it one.
Two templates you can steal
Tracking car (subject moves, camera follows)
- Subject: Matte black sports coupe parked at a mountain overlook, engine idling, exhaust heat shimmer.
- Motion: Car accelerates forward along the empty asphalt road, tires kicking up dust.
- Camera: Low-angle tracking camera pushes forward alongside the driver side at matching speed.
- Lighting: Warm golden hour rim light, long shadows across the road.
Character reveal
- Subject: Studio portrait of a female ceramicist in her late 30s with clay on her hands, neutral expression.
- Motion: She slowly looks up from her work toward the lens and blinks.
- Camera: Slow push-in on 85mm lens, shallow depth of field.
- Lighting: Soft north-facing window light from camera left, dark muted workshop background.
Notice what’s missing: no “epic,” no “stunning,” no “4k masterpiece.” Each line does one thing. Also notice that the car template quietly keeps the road straight. The mountain curve from the broken prompt is gone, because a curve would be a fifth movement. If you want the curve, make it its own shot and cut the two together in editing.
The golden rule
Never mix camera motion with character motion in the same sentence. Split them.
- If the camera orbits, keep the subject still or walking in a straight line.
- If the subject does something complex, like turning, gesturing, and speaking, lock the camera to a static tripod or a single-axis push.
One thing moves a lot at a time. The other one stays calm. A good gut check is to ask which element the viewer should be watching. That one gets the motion budget, and everything else gets locked down.
How to try it today
- Take your last failed prompt and count the movements in it. Subject, camera, environment, all of them.
- Pick the one that matters most for the shot.
- Rewrite the prompt into the four parts above, with one verb and one camera axis.
- Generate. If it still warps, simplify the motion before you add any detail. Change only one thing between attempts, so you know which edit actually fixed it.
- Save the versions that work. Over time you build your own library of shots you can reuse, and a new project starts from a proven template instead of a blank box.
The author also asked a good open question: do negative prompts help here, or is it better to handle everything through camera constraints? Their approach leans on constraints, and that’s the side I’d test first. Fewer instructions beat more warnings.
Try the 4-part structure on one shot this week and see how many credits you save. Then tell us what worked for you in Kling or Gen-3!
Why video models keep morphing your shots (and the 4-part syntax that fixed it for me)
by u/Signal-Chipmunk-9634 in PromptEngineering