The Complete Guide to AI Video Prompt Engineering: From 'Gacha' to 'Precision Control'

Have you ever done the math — how much time have you spent on "pulling the gacha"?

Open an AI video tool, type in a description, generate, unsatisfied, tweak a few words, generate again, still unsatisfied... Loop a dozen times, and finally settle for a clip that's just good enough. If you're still making AI videos this way in 2026, you're truly wasting the tools of this era.

The truth is: AI video generation isn't mysticism — it's an engineering discipline that can be mastered systematically. Those creators you envy who "get the shot in one try" aren't luckier than you; their prompts are better than yours. Today's article will break down this craft for you, piece by piece.

1. The Underlying Logic of AI Video Prompts

Before learning how to write, you must first understand a core concept: AI video models aren't "understanding" your text — they're "translating" your text.

When you write "a girl running in the rain," the model does three things:

• Scene parsing: identifies the three entities "girl," "rain," and "running"
• Relationship modeling: builds relationships between "girl–running" (action) and "rain–scene" (environment)
• Visual mapping: maps the text description to the corresponding visual patterns in the training data

The question is: does your prompt provide enough information for these three steps? If not, the model fills in the gaps with its "default template" — and the default template is usually the most mediocre, most AI-looking version.

A Good Prompt = Constraints × Creative Space

Here's a counterintuitive formula. Many beginners think the freer the prompt, the better. But in reality, the more constraints, the better the results. Because constraints reduce the model's "choice space," making it easier to make the right decisions.

Take a look at the comparison between two prompts:

❌ Beginner version:

A girl running in the rain, cinematic feel

✅ Advanced version:

A 25-year-old Asian woman, black hair wet and clinging to her cheeks,
wearing a dark blue trench coat and a white shirt, running across Shibuya Crossing in Tokyo in the evening.
Moderate rain, raindrops clearly visible. Warm-yellow streetlights create a halo through the rain.
Camera: 35mm prime, side tracking shot, low angle looking up.
Color tone: cold blue + warm orange contrast, German-style cinematic color grading.
Facial expression: determined with a touch of urgency, lips pressed tight, eyebrows slightly furrowed.

Why is the second one better? Because it provides the model with:

• Character details: age, hairstyle, clothing, expression (constrains character appearance)
• Environment details: time, place, weather, lighting (constrains scene atmosphere)
• Camera language: focal length, camera position, angle (constrains visual presentation)
• Color style: tone, grading style (constrains color tendency)

Each layer of information narrows the model's "guess space," so the final output is much closer to what you have in your mind.

2. The Seven-Layer Structure for Prompts

After extensive practice (and countless failures), I've summarized a prompt structure that works for almost all AI video models — the Seven-Layer Method:

Layer 1: Subject Description (Who/What)

Describe the core subject in the frame: person, object, animal. Include appearance, clothing, posture, expression.

A middle-aged male scientist in a white lab coat, wearing gold-rimmed glasses,
hair grizzled but eyes sharp, hands resting on the lab bench.

Layer 2: Action Description (Action)

What is the subject doing? What is the amplitude, speed, and rhythm of the movement?

He slowly raises his head, takes off his glasses, pinches the bridge of his nose with his thumb and index finger,
then takes a deep breath and puts the glasses back on. The movements are steady and unhurried.

Layer 3: Scene and Environment (Where/When)

Time, place, spatial characteristics.

A biological laboratory late at night, 2 a.m. He is alone in the room.
Around him are glowing blue experimental devices and petri dishes.
Outside the window, city lights flicker in the distance.

Layer 4: Light and Atmosphere (Lighting/Mood)

Light source direction, light quality, color temperature, overall mood.

The main light comes from the blue LED culture lamp on the lab bench, shining upward onto his face,
creating dramatic underlighting with sharp shadows. The rest of the room is in semi-darkness.
Overall mood: lonely, focused, with a touch of a mad scientist's obsession.

Layer 5: Camera and Composition (Camera/Composition)

Focal length, camera position, movement, composition rules.

Lens: 50mm prime, wide aperture f/1.8, shallow depth of field.
Camera position: front medium close-up, at eye level with the subject.
Movement: extremely slow push-in, mimicking handheld breathing.
Composition: subject slightly right of center, left side left open for the lab equipment.

Layer 6: Style and Texture (Style/Texture)

Visual style, texture, post-processing direction.

Style: referencing the visual aesthetic of *Blade Runner 2049*.
Texture: digital film texture, subtle film grain.
Post direction: teal-orange color grading, high contrast, details preserved in the shadows.

Layer 7: Technical and Parameters (Technical)

Resolution, frame rate, aspect ratio, special requirements.

Output: 4K resolution, 24fps cinematic frame rate, 2.35:1 widescreen aspect ratio.
Special requirements: glowing objects in the frame should produce a soft bloom effect.

Complete Example (All Seven Layers Combined)

When you merge all seven layers into a single prompt, here's the result:

A middle-aged male scientist in a white lab coat, wearing gold-rimmed glasses, with grizzled hair but sharp eyes.

He slowly raises his head, takes off his glasses, pinches the bridge of his nose with his thumb and index finger, then takes a deep breath and puts the glasses back on.

A biological laboratory late at night, 2 a.m. Around him are glowing blue experimental devices and petri dishes. City lights flicker in the distance outside the window. The main light comes from the blue LED culture lamp on the lab bench, shining upward onto his face, creating dramatic underlighting.

Lens: 50mm prime, f/1.8, front medium close-up, extremely slow push-in with handheld breathing.

Style references *Blade Runner 2049*, teal-orange grading, digital film texture, 4K/24fps/2.35:1 widescreen.

This prompt is about 300 characters, which looks long, but it follows the "constraints × creative space" principle — every information block helps the model make the right decisions. Tested on Kling 3, this prompt produces a usable clip within three tries.

3. Model-Specific Prompt Strategies

The Seven-Layer Method is a general framework, but different models are sensitive to different types of descriptions. You need to adjust based on the model's characteristics:

Kling 3 Optimization: Emphasize Action Descriptions

Kling 3 is extremely sensitive to action details. If you write "turn around" in your prompt, it's just a turn; if you write "slowly turn around, first rotate the shoulder, then drive the waist, and finally move the feet," it can generate a layered turn.

Kling 3 golden rule: The more detailed the action description, the more stable the output.

Optimized prompt fragment for Kling 3:

Movement: She doesn't turn directly. Instead, she first looks slightly back over her right shoulder,
then rotates her right shoulder backward 45 degrees, driving the entire upper body to turn.
Her left foot stays planted as a pivot, while her right foot follows the body rotation and steps backward,
finally completing the full turn. The whole process takes about 3 seconds.

Veo 3.1 Optimization: Strengthen Text and Structural Descriptions

Veo 3.1 is sensitive to structured information. If you need precise composition, text placement, or multi-element layout, bullet-point descriptions work better than natural paragraphs:

Optimized prompt for Veo 3.1:

Frame structure (top to bottom):
- Top 20%: title "THE FUTURE IS NOW", white sans-serif font, centered
- Middle 60%: main visual — an astronaut walking on the Martian surface, long shot
- Bottom 20%: gradient black mask at bottom, no text

Sora 2 Optimization: Use Imagery and Analogies

Sora 2's "artistic intuition" is very strong. You don't need to tell it exactly how to do things; just give it an aesthetic direction and a feeling:

Optimized prompt for Sora 2:

This scene should recall Terrence Malick's films —
light flowing like honey, every shot like a photograph that could hang in a gallery.
Not documenting reality, but capturing the poetry of reality.

Seedance 2.0 Optimization: Specify Style Anchors

Seedance 2.0 is extremely sensitive to style keywords. Don't just say "Chinese style"; be precise about the specific school or style:

Optimized prompt for Seedance 2.0:

Style: Northern Song Dynasty imperial academic landscape painting, referencing Fan Kuan's *Travelers Among Mountains and Streams*.
Features: high-distance composition, raindrop texture strokes (cun), primarily ink wash with light ochre rendering.
Motion: mist and clouds drifting slowly among the mountains, a waterfall cascading down, pine branches swaying slightly in the wind.

MiniMax Optimization: Keep It Simple and Direct

MiniMax has limited ability to digest complex descriptions. Prompts under 100 characters work best. Focus on the "subject + action + scene" layers, without too much embellishment:

Optimized prompt for MiniMax:

A young person sitting in a coffee shop working on a laptop, sunlight streaming in through the window.
Medium shot, static camera, natural colors.

4. Prompt Practice: 5 Ready-to-Use Templates

The following templates have been validated on Kling 3, Veo 3.1, and Sora 2. You can use them directly or modify them for your needs:

Template 1: Product Showcase

[Product] placed on a [material/color] surface, lit by [lighting style] from [direction].
Camera performs [movement], [speed description].
Background is [color/scene], blurred.
Aspect ratio [ratio], overall style [style keyword].
Highlights have [special light effect description].

Template 2: Character Narrative

[Character description: age, appearance, clothing], currently [action details].
Scene is [time + place + environmental features]. Light comes from [light source description], creating [light and shadow effect].
Camera: [focal length] + [camera position] + [shot size] + [movement].
Facial expression: [specific expression description, detailed down to eyebrows and mouth corners].
Color tone: [color tendency], overall mood [emotion keyword].

Template 3: Landscape / Establishing Shot

[Location/scene] under [time/season/weather].
Frame starts with [initial composition], camera does [movement].
Light [angle + quality], [special lighting effect].
Color tone [style], [reference work or photographer].

Template 4: Text / Title Animation

The text "[specific text content]" appears on the screen at [position] using [appearance method].
Text style: [font characteristics] + [material texture] + [color].
Background is [scene description], creating [contrast relationship] with the text.
Animation lasts [duration], rhythm [fast/slow/ease in/out].

Template 5: Creative Transition

Scene A: [starting scene description], transition begins after [duration].
Transition method: [specific description of how A becomes B].
Scene B: [ending scene description].
During transition: [visual characteristics of the intermediate state].
Overall style: [style keyword], transition smooth and natural.

5. Pitfall Guide: The 5 Most Common Prompt Mistakes

Mistake 1: Too Many Abstract Adjectives

❌ "A beautiful, breathtaking, epic sunset landscape"

✅ "At sunset, an orange-red sun sits on the horizon, the sky graduates from deep purple to golden yellow. Palm trees in backlight form black silhouettes. Low-angle wide shot with a sparkling sea in the foreground."

Why it's wrong: Words like "beautiful," "breathtaking," and "epic" mean nothing to the AI — it has seen a million variations of "beautiful" sunsets in its training data, but it doesn't know which one you want.

Mistake 2: Contradictory Descriptions

❌ "A bright night, moonlight illuminating the entire room, while outside the window is sunny"

Why it's wrong: Don't give the model a split personality. A scene can only have one main light source.

Mistake 3: Ignoring What You Don't Want

Many models support negative prompts. Make good use of them:

Negative prompt: text, subtitles, logo, watermark, blur, distortion, extra fingers, unnatural facial expressions, overexposure, underexposure

Kling 3 and Seedance 2.0 both support negative prompt fields — don't waste them.

Mistake 4: Prompts That Are Too Short

Back in 2024, a 50-character prompt might have been enough. But models in 2026 are stronger — they can digest 300-character detailed descriptions and convert them better. The more detailed you write, the fewer failures you'll have.

Mistake 5: Not Iterating on Prompts

Every failed generation is a data point. Don't just click "generate" again — analyze where the problem lies and revise the prompt. Build a "prompt → problem → fix" loop:

Version 1: A girl running in the rain → subject is blurry
Version 2: An Asian woman running in the rain, medium shot → stiff movement
Version 3: A 25-year-old Asian woman, black hair wet against her face, running in the evening rain,
           side tracking shot, 35mm lens → satisfactory result

Conclusion: Prompts Are the Language Between You and AI

Many people think the future of AI video generation is "one sentence creates a movie" — you don't need to learn any techniques because AI will handle everything on its own. But I believe this idea is unrealistic for at least the next five years.

Prompt engineering is not an "extra skill" you pick up just to use AI. It's a language you use, as a creator, to express the images in your mind through AI. The more fluently you master this language, the more faithfully AI becomes your brush.

If you don't want to write prompts from scratch, Tomato AI (cctocv.com) offers a smart prompt optimization feature — you enter a simple creative description, and the platform automatically expands it into a structured, professional prompt. It also supports one-click generation across multiple models including Kling 3, Veo 3.1, Sora 2, and Seedance 2.0. Let the tool save you the "translation" step, so you can focus purely on your creativity.

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Produced by the Tomato AI content team. Visit cctocv.com to experience intelligent AI video creation.