The Art and Science of AI Video Prompts: From Random Luck to Systematically Improving Generation Quality

You write a prompt: "A cat sunbathing on the windowsill." The AI generates a cat-shaped mosaic sitting on something unrecognizable. You revise it five times—each result is different, but none of them works. You start to wonder: Is AI video just a matter of luck? Why can someone else make cinematic shots with the same model, while you can't even get a "cat" right? Don't worry. It's not bad luck—you simply haven't grasped the underlying logic of AI video prompting.

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1. First, Understand How AI "Reads" Your Prompt

AI video models don't "understand" your prompt—they perform probabilistic mapping.

When you write "A cat sunbathing on the windowsill," the AI:

• Maps "cat," "windowsill," and "sun" to the corresponding visual features in its training data.
• Maps "on" and "sunbathing" to spatial relationships and lighting patterns.
• Combines those features in latent space to generate a sequence of frames that satisfies all its constraints.

Key Insight: AI's "comprehension precision" differs across word categories.

Word Type	AI Comprehension Precision	Examples
Object nouns	★★★★★ Highest	cat, car, table, cup
Colors / materials	★★★★☆ High	red, metal, glass, plush
Lighting conditions	★★★★☆ High	sunlight, backlight, neon, soft light
Spatial relationships	★★★☆☆ Medium	above, beside, through
Action descriptions	★★★☆☆ Medium	walk, run, turn head, fall
Emotion / atmosphere	★★☆☆☆ Low	sad, serene, tense
Abstract concepts	★☆☆☆☆ Lowest	freedom, future, love

This means: Put the "semantic weight" of your prompt on the high-precision words that AI understands best, and use low-precision words only as auxiliary modifiers—not the other way around.

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2. The Golden Prompt Structure: The Five-Layer Onion Model

Think of a prompt as an onion with five layers from the inside out. Each layer solves one problem, and together they stack up.

[Layer 1: Subject] → What's in the frame? (nouns)
[Layer 2: Action] → What is the subject doing? (verbs)
[Layer 3: Environment] → Where is it? What light? What color tone?
[Layer 4: Camera] → How is it shot? What shot size? What movement?
[Layer 5: Style] → What texture? What style? What aspect ratio?

Full example:
[an orange tabby cat] [stretching lazily on the windowsill, its tail slowly swaying]
[warm afternoon sunlight slants in from the left window, casting soft light patches on the wooden floor]
[medium shot, slow push-in toward the cat's face]
[cinematic color grading, shallow depth of field, film grain, 8mm vintage style, 1080P]

Writing Principles for Each Layer

Layer 1—Subject (must be precise):

• ✅ "An orange short-haired cat" > "A cat"
• ✅ "A young Asian woman wearing a white shirt" > "A person"
• ❌ Avoid vague references: "someone," "some stuff"

Layer 2—Action (must be simple):

• ✅ Single action: "Slowly turns its head" > "turns head, stretches, licks paw, jumps down"
• Golden rule: One prompt should describe only one core action. Multiple actions distract the AI, so none of them gets done well.
• Speed modifiers are critical: "slowly," "quickly," "in slow motion." If you don't specify speed, the AI defaults to medium speed.

Layer 3—Environment (setting + light + color tone):

Lighting templates (choose one):
- Natural light: "warm golden afternoon sunlight" / "soft overcast diffused light" / "cold blue early-morning light"
- Artificial light: "warm table lamp from a 45-degree angle" / "neon light casting from below"
- Special light: "backlit silhouette" / "side light emphasizing texture" / "top light for a moody feel"

Color tone templates (choose one):
- "cinematic tone" (high contrast, warm shadows)
- "Japanese fresh tone" (low saturation, blue-green tint)
- "cyberpunk tone" (blue-purple + magenta, high saturation)
- "warm retro film tone" (yellowish, soft highlights)

Layer 4—Camera (shot size + movement):

Shot size quick reference:
- Extreme Close-up → face, product details
- Medium Shot → half body, everyday conversation
- Wide Shot → full scene, environmental storytelling
- Tracking Shot → follows the moving subject
- Overhead Shot → shot from above, god's-eye view

Camera movement quick reference:
- Static → "fixed shot"
- Push in → "slow push-in"
- Pull back → "slow pull-out"
- Pan → "horizontal pan"
- Orbit → "rotate around the subject"

Layer 5—Style (texture + format):

Style parameters (stack together):
- Texture: "4K," "cinematic," "film grain"
- Style: "realistic," "anime style," "watercolor style"
- Special: "shallow depth of field," "macro," "time-lapse"
- Format: "1080P," "9:16 vertical," "16:9 widescreen"

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3. Prompt Preferences of Different Models

The same prompt can produce very different results across models, because their training data and architectural tendencies differ.

Kling 3: Native Chinese, Emphasizes "Word Frequency"

Kling 3 has the best Chinese comprehension among the five models. It favors high-frequency Chinese words and clear composition instructions.

Kling 3 preferred style:
An orange tabby cat lying on the windowsill, dozing in the sun with half-closed eyes,
the scene is warm and serene, its fur glowing in the light,
the camera slowly pushes in for a close-up of the cat's face, revealing whisker details,
warm color tone, 1080P

Key: Use high-frequency Chinese words. Kling understands atmosphere words like "cozy" and "serene" very well.

Veo 3.1: Image-Quality Ceiling, Prefers "Physical Accuracy"

Veo 3.1 surpasses other models in physical realism, but its prompts need more "technical" descriptions.

Veo 3.1 preferred style:
An orange tabby cat sleeping on a wooden windowsill,
golden hour sunlight streaming through the window from the left side,
dust particles floating in the light beam visible,
the cat's fur has visible individual strands with subsurface scattering where light hits the edges,
slow camera push-in, shallow depth of field, f/2.8 aperture,
cinematic color grading, photorealistic, 1080P

Key: Describe physical phenomena (dust particles, subsurface scattering, aperture value). Veo understands these technical parameters.

Seedance 2.0: Multimodal King, Prefers "Reference Anchoring"

Seedance 2.0's core strength is the syntax and multimodal referencing. Prompts should use reference images to lighten the descriptive burden.

Seedance 2.0 preferred style:
The cat in <Image 1> slowly stretches on the windowsill in <Image 2>,
light enters from the window side in <Image 2>,
the cat's movements are natural, and the fur texture stays consistent with <Image 1>,
medium shot, shallow depth of field, 1080P

Key: Information already conveyed through images doesn't need to be repeated in text.
Text should focus on "motion" and "change."

Sora 2: Physics Engine, Prefers "Motion Continuity"

Sora 2's physical realism comes from its built-in physics simulation. Its prompts should focus on describing the continuous motion process.

Sora 2 preferred style:
A cat walking along a windowsill, each paw placed deliberately,
the cat turns its head slowly to look out the window,
its tail swishes once then settles, ears rotate forward,
maintaining continuous motion throughout the 10-second shot,
natural window light, photorealistic, 1080P

Key: Describe a continuous motion process. Sora excels at keeping motion natural over long sequences.

MiniMax: Speed First, Prefers "Simplicity"

MiniMax is good at fast generation, but prompts must be extremely concise—longer prompts often introduce problems.

MiniMax preferred style:
Orange tabby cat on the windowsill sunbathing, napping, warm light, shallow depth of field, 1080P

Key: The shorter, the better. Just deliver the core elements.

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4. From "Gambling on Luck" to "Reproducible": Build a Prompt-Tuning System

Step 1: Form a "Version Log" Habit

Record every generation: prompt version + model + result assessment.

| Version | Key Prompt Difference | Model | Score | Issue |
|---------|----------------------|-------|-------|-------|
| v1 | "a cat" | Kling 3 | 2/5 | breed uncontrollable |
| v2 | "an orange short-haired cat" | Kling 3 | 3/5 | stiff movement |
| v3 | v2 + "slowly turns head" | Kling 3 | 4/5 | cluttered background |
| v4 | v3 + "clean white background" | Kling 3 | 5/5 | ✅ |

Step 2: Identify Your "Failure Modes"

Classify each failure into a "failure mode" so you can avoid it next time by addressing the root cause:

Failure Mode	Symptom	Root Cause	Solution
Ambiguous subject	Can't tell what's in the frame	Subject description is not specific enough	Add object details (breed, material, color, shape)
Jittery action	Movements twitch, look unnatural	Action description is too complex	Simplify to one action + speed modifier
Messy frame	Background elements are chaotic	Missing environmental constraints	Explicitly define background and spatial relations
Character morphing	Same person looks different across shots	No character anchoring	Use Seedance 2.0 to maintain consistency
Degraded quality	Visual quality drops during the video	Too much motion	Reduce motion speed and camera movement
Color drift	Overall color tone gradually shifts	Unstable lighting description	Specify lighting conditions in both first and last frames

Step 3: Build a Prompt Component Library

Store validated "phrases that work" and reuse them next time:

# Lighting Library
"golden hour backlight" → warm golden-hour backlight, suits cozy moods
"soft diffused window light" → soft window light, suits people/products
"neon street lighting with rain reflection" → neon streetlight + rainy reflections, cyberpunk

# Motion Library
"slow gentle pan left to right" → slow horizontal pan, a safe movement
"subtle breathing movement only" → barely noticeable breathing motion; the safest for product display
"smooth 15-degree per second rotation" → 15°/sec rotation; standard speed for product showcase

# Color Tone Library
"Kodak Portra 400 color palette" → Kodak Portra 400 palette, vintage warm look
"Wes Anderson symmetrical pastel" → Wes Anderson symmetrical pastel palette
"Blade Runner teal and orange" → Blade Runner teal-and-orange contrast

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5. Quick-Reference Prompt Templates by Use Case

Use Case 1: Product Showcase

[Product name/description] on [plain/simple background] [slowly rotates/pans],
the [gloss/texture] of [key material] is clearly visible under [lighting],
camera [movement], [shot scale], [color tone], 1080P

Use Case 2: Character Narrative

A(n) [age] [gender], [appearance features], [clothing],
performs [single action] in [setting], [emotion/expression],
[lighting conditions], [atmosphere description],
[shot scale], [color tone], 1080P

Use Case 3: Landscape / B-Roll

[location/scene] in [time/weather],
[main visual element] is [position/movement] in the frame,
[lighting characteristics], [atmosphere description],
[shot scale, usually wide], [color tone], [special effect such as time-lapse], 1080P

Use Case 4: Abstract / Artistic

[core visual metaphor] expresses [abstract concept],
[colors] and [shapes] [how they transform],
[style reference], [texture quality],
[aspect ratio], 1080P

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6. The Ultimate Mindset: Good Prompts Are Subtraction, Not Addition

Beginners often believe that the longer the prompt, the better, so they pile in every word they can think of.

The truth: Every imprecise descriptive word adds a dimension of noise to the AI's semantic space.

❌ Overloaded Version (147 characters):
A cute orange short-haired cat, adorable and healing, on a warm and cozy wooden windowsill,
sunlight sprinkles down, the cat looks very content, lazily stretching,
with a pot of green plants beside it, blue sky and white clouds outside, a few birds flying past,
the picture is poetic and warm, cinematic feel, hoping for that Japanese fresh vibe,
preferably shallow depth of field with a blurred background, 1080P

✅ Refined Version (78 characters):
An orange short-haired cat lazily stretches on a wooden windowsill, tail curling slightly,
afternoon sunlight slants in from the left window, light patches falling on the cat's back and the sill,
the camera slowly pushes in to the cat's face, shallow depth of field, clean background,
warm cinematic tone, 1080P

The difference isn't in writing flair—it's in "information density." In the refined version, there is a precise piece of usable information every 7–8 characters. In the overloaded version, there is a vague piece of information every 20 characters, and everything in between is noise.

Remember this formula:

Generation quality = Prompt information density × Model fit / Semantic noise

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Summary

AI video prompting is not mysticism. It's an engineering discipline that can be broken down, optimized, and reused.

The five-layer onion (Subject→Action→Environment→Camera→Style), the four model tendencies (Kling for Chinese, Veo for technical detail, Seedance for multimodal references, MiniMax for simplicity), and the three failure-mode patterns—use these frameworks, and your "usable output rate" can rise from 20% to over 60%.

The final mindset, in essence: Quality over quantity—less is more.

Open Tomato AI and rewrite a prompt using the templates above—one that previously failed you. You'll realize that the image in your mind was never limited by imagination—it was limited by your ability to "translate it into a language AI can understand." And starting now, that ability has been broken down into teachable modules.