Wan AI Text-to-Video Prompt Guide: Complete Tutorial with Prompt Examples

Introduction

I've been working with Wan AI video generation since the 2.1 release, and the one question I get asked more than any other is: "What should I actually write in my prompts?"

It's a fair question. AI video is different from AI image prompting. You're not just describing a static scene — you're directing motion, timing, camera movement, and continuity across frames. A text prompt that works perfectly for an image will often produce a jittery, inconsistent video when fed into a generator.

I've run hundreds of Wan text-to-video generations across different versions (2.1, 2.2, and now 2.7), and I've developed a prompt formula that consistently produces better results. This guide shares the exact structure, examples, and techniques I use.

Whether you're creating cinematic short films, product demos, social media content, or experimental visuals, these prompt strategies will get you better outputs from Wan AI.

TL;DR

• Structure your prompts in three parts: subject description + environment/setting + motion/camera instruction
• Motion verbs are critical — Wan interprets action words like "walking," "flowing," "rotating" more reliably than abstract concepts
• Camera instructions work — add "slow pan," "dolly zoom," "tracking shot" to control the visual perspective
• Keep clips under 5 seconds for better consistency; longer clips increase the risk of character drift and artifacts
• Avoid overly complex scenes — Wan handles 2–3 main elements best; more than that increases the chance of confusing motion
• Use negative prompts sparingly — Wan doesn't have robust negative prompt support yet; focus on clear positive instructions
• Test with the Wan Text-to-Video Generator to iterate quickly on prompts

Why Prompting for Video Is Different from Image

When you prompt an AI image generator, you're describing a single moment — a frozen frame. The model only needs to understand what that one instant should look like.

When you prompt an AI video generator, you're describing a sequence of moments. The model needs to understand:

1. What the scene contains (subjects, objects, environment)
2. How things move (direction, speed, style of motion)
3. How the camera behaves (static, panning, zooming, tracking)
4. How things change over time (transitions, unfolding actions, evolving states)

Most failed Wan generations happen because the prompt describes the "what" but not the "how." Here's an example:

❌ Weak prompt: "A cat sitting on a table"

This produces a video where the cat may flicker or morph because the model doesn't know what motion to generate — it only knows there's a cat and a table.

✅ Strong prompt: "A gray cat sitting on a wooden table, slowly blinking and turning its head to the right. Soft window light. Static camera, shallow depth of field."

This gives Wan clear instructions for the subject, the motion, the lighting, and the camera behavior. The result is significantly more coherent.

The Wan Prompt Formula

After extensive testing, I've settled on a three-part structure that works reliably:

[Subject & Appearance] + [Setting & Atmosphere] + [Motion & Camera]

Part 1: Subject & Appearance

Describe what's in the scene and how it looks. Be specific about:

• Main subject: Type (person, animal, object), appearance (color, texture, size)
• Secondary elements: Supporting objects or characters
• Composition: Where things are positioned relative to each other

Good: "A young woman with long brown hair wearing a red vintage dress"

Better: "A young woman with long brown hair, freckles, and a red 1950s vintage dress with white polka dots"

Part 2: Setting & Atmosphere

Describe where the scene takes place and the mood. Include:

• Location: Indoor/outdoor, specific environment
• Lighting: Natural, cinematic, moody, bright, golden hour
• Weather/atmosphere: Rain, fog, sunny, cloudy
• Color palette: Warm tones, cool tones, monochrome

Good: "In a Parisian cafe courtyard, afternoon sunlight"

Better: "In a Parisian cafe courtyard with ivy-covered walls, warm golden afternoon sunlight filtering through leaves, dappled shadows on the table"

Part 3: Motion & Camera

This is the most important part for video. Specify:

• Subject motion: What the character/object does
• Motion style: Slow, fast, smooth, jerky, flowing
• Camera movement: Static, pan left/right, tilt up/down, dolly in/out, tracking
• Temporal changes: Things that happen over time (unfolding, revealing, transforming)

Good: "Walking slowly toward camera"

Better: "Walking slowly toward camera with a gentle smile, dress flowing in the breeze. Slow dolly backwards, maintaining fixed distance. Cinematic 24fps motion feel."

Putting It All Together

Complete prompt: "A young woman with long brown hair wearing a red vintage dress with white polka dots, walking slowly through a Parisian cafe courtyard with ivy-covered walls. Warm golden afternoon sunlight filtering through leaves, dappled shadows. Walking slowly toward camera with a gentle smile, dress flowing in the breeze. Slow dolly backwards, maintaining fixed distance. Cinematic, soft focus background."

Prompt Templates by Video Type

1. Cinematic / Narrative

Best for storytelling, short films, and emotional scenes.

Template:

[Character description] [in a specific setting with atmospheric detail]. [Action with emotional quality]. [Camera movement]. [Lighting and mood].

Example prompt: "An elderly man with a white beard wearing a knitted sweater, sitting on a wooden bench in a misty autumn park. Holding a letter and looking up toward the sky with a melancholic expression. Fallen leaves drifting in the wind around him. Slow push-in zoom. Soft overcast lighting, muted earth tones, shallow depth of field."

2. Product / Commercial

Best for product demos, advertising content, and e-commerce visuals.

Template:

[Product type and appearance] [in a clean setting]. [Product in motion or being used]. [Camera circling or steady shot]. [Studio lighting or natural light].

Example prompt: "A matte black ceramic coffee mug with minimalist design sitting on a light oak wooden table. Hot coffee inside with gentle steam rising in spiral patterns. A hand reaches into frame and slowly wraps around the mug. Slow orbiting camera around the mug. Soft studio lighting from the left, warm tones."

3. Nature / Landscape

Best for atmospheric landscape shots, time-lapse effects, and environmental scenes.

Template:

[Landscape type] [with specific terrain and vegetation]. [Atmospheric conditions]. [Natural motion: water, wind, clouds]. [Slow camera movement].

Example prompt: "A wide shot of a misty mountain lake at sunrise. Pine trees lining the shoreline. Mist slowly rising from the surface of the water. Gentle ripples spreading across the lake. Light clouds drifting across the sky. Extremely slow horizontal pan from left to right. Soft pink and orange sky colors reflecting on the water surface."

4. Abstract / Artistic

Best for experimental visuals, music videos, and creative projects.

Template:

[Material or visual style] [with specific shapes/colors]. [Movement description]. [Transformation or evolution]. [Texture and lighting detail].

Example prompt: "Fluid abstract shapes in iridescent colors flowing across a dark background. Bioluminescent patterns pulsing and shifting like living organisms. Slow organic morphing between geometric and fluid forms. Deep blue and purple base with electric cyan highlights. Smooth continuous motion, no hard edges. Cinematic lighting with volumetric glow effects."

5. Character Animation

Best for character-focused content, talking avatars, and persona-driven videos.

Template:

[Character appearance and style] [in a defined setting]. [Character action with emotional context]. [Facial expression detail]. [Camera focused on character].

Example prompt: "A cheerful young man in his late 20s with short dark hair and glasses, wearing a blue casual button-up shirt, sitting at a modern desk in a bright home office. Natural plants visible in the background. Talking to camera with enthusiastic hand gestures, smiling naturally. Medium shot, camera steady at eye level. Bright natural lighting from a window on the left."

Common Prompt Mistakes and How to Fix Them

Mistake 1: No Motion Instruction

❌ "A beach at sunset with waves" ✅ "A beach at sunset with waves slowly rolling onto the sand. Foam bubbling at the shoreline. Gentle sea breeze creating ripples. Slow horizontal pan along the coastline."

Why it matters: Without motion instructions, Wan generates minimal movement, often resulting in a near-static image rather than a video.

Mistake 2: Too Many Elements

❌ "A busy city street with cars, buses, bicycles, pedestrians, street vendors, neon signs, and a food market in the background. A red double-decker bus driving past. A street musician playing guitar. Dogs walking with owners. Steam rising from food stalls." ✅ "A busy city street at dusk with moderate pedestrian traffic. A red double-decker bus driving slowly from left to right. Neon signs glowing in the background. Steady medium-wide shot, subtle camera drift."

Why it matters: Wan handles 2–3 main elements best. Overcrowding the scene causes objects to merge, flicker, or behave unpredictably.

Mistake 3: Vague Motion Descriptions

❌ "A flower blooming" ✅ "A red rosebud slowly opening its petals over 5 seconds. Each petal unfurling outward in sequence with a gentle spiraling motion. Macro close-up shot, shallow depth of field, soft dewdrops on petals. Static camera."

Why it matters: "Blooming" is too abstract. Wan needs concrete descriptions of what moves, how it moves, and over what duration.

Mistake 4: Ignoring Lighting and Atmosphere

❌ "A person reading a book" ✅ "A person reading a book in a cozy library corner. Warm lamp light illuminating the pages, soft shadows around the reader. Pages turning slowly. Gentle firelight flickering in the background. Medium shot, subtle camera drift."

Why it matters: Lighting gives the scene depth and mood. It helps Wan generate consistent shadows and highlights across frames. For a closer look at how it stacks up against other models, see Gemini Omni vs Wan 2.7.

Advanced Prompt Techniques

Using Style References

Wan can adopt specific visual styles when you describe them in the prompt. Try adding:

• "Studio Ghibli animation style"
• "Wes Anderson symmetrical composition"
• "Blade Runner 2049 neon-noir aesthetic"
• "National Geographic documentary style"
• "Claymation stop-motion texture"

Note: Style references work best when combined with specific visual elements (color palette, lighting, composition) rather than just the style name alone.

Controlling Timing and Rhythm

Specify the pace of motion to match your desired video feel:

• Slow/elegant: "Slow motion, gentle, flowing, deliberate, unhurried"
• Fast/energetic: "Fast-paced, rapid, energetic, quick cuts implied"
• Rhythmic: "Pulsing, rhythmic movement, timed to music, beat-driven"

Creating Scene Transitions

If you're planning a sequence of connected videos, use consistent descriptors across all prompts:

• Same character: Use identical appearance descriptions in every prompt
• Same location: Reuse environmental descriptors
• Consistent lighting: Maintain the same lighting keywords

This significantly improves the coherence of multi-shot sequences when edited together.

Testing Your Prompts

The fastest way to improve your Wan prompting is to iterate quickly. Here's my workflow:

1. Start with the template — Use the three-part formula for your first generation
2. Review the output — Note what worked and what didn't (motion, consistency, artifacts)
3. Adjust one variable at a time — Change the motion description first, then the setting, then the subject
4. Save your winning prompts — Build a library of prompts that work for different video types

The Bottom Line

Wan AI video generation is powerful, but the quality of your output is directly tied to the quality of your prompt. The three-part formula — Subject + Setting + Motion — gives you a reliable framework for getting consistent results.

The key lessons I've learned from hundreds of generations:

• Be specific about motion — "walking slowly toward camera" is infinitely better than "moving"
• Limit scene complexity — 2–3 main elements per prompt for best results
• Add camera instructions — Wan responds well to camera movement directives
• Iterate fast — Test variations of your prompts and refine based on output
• Build a prompt library — Save what works so you can reuse and adapt

Start with the templates in this guide, run your first few tests, and you'll quickly develop a feel for what Wan responds to best. Every model has its own prompt language — Wan's is structured, motion-focused, and benefits from clear direction.

Related guides

• Gemini Omni vs Wan 2.7: Which AI Video Model Should Creators Use?
• GPT Image 2 vs Free AI Image Generators: Complete Comparison Guide for 2026
• LTX 2.3 vs Wan 2.7: Complete Comparison Guide for AI Video Creators (2026)

FAQ

What is Wan AI text-to-video?

Wan AI is an AI video generation model developed by Alibaba's Tongyi Lab. Text-to-video allows you to input a text description and generate a video clip based on that prompt, typically 5–10 seconds in length.

What makes a good Wan AI prompt?

A good prompt includes three elements: a clear subject description, a defined setting with atmosphere, and specific motion/camera instructions. Avoid vague action words and overcrowded scenes.

How long should Wan videos be?

Wan AI typically generates 5–10 second clips. Shorter clips (under 5 seconds) tend to have better consistency and fewer artifacts. For longer content, generate multiple short clips and edit them together.

Can Wan AI follow camera movement instructions?

Yes — Wan responds well to camera directions like "slow pan left," "dolly forward," "tracking shot following subject," and "push-in zoom." Be specific about the type and speed of camera movement.

What's the best way to get consistent characters?

Use the same detailed character description in every prompt. Include specific clothing, hair color, facial features, and any distinguishing marks. For best results, use Wan's image-to-video feature with a reference image to anchor the character's appearance.

Does Wan support negative prompts?

Wan's negative prompt support is limited compared to image generation models. Focus on writing clear positive instructions rather than relying on negative prompts to fix issues.

Is Wan text-to-video free to use?

Wan offers free generations through various platforms including wanvideogenerator.com with free credits available on signup.

References

• Wan Text-to-Video Free Generator — Test your prompts with the latest Wan model
• Wan 2.7 AI Video Generator — Access Wan 2.7 via Pollo.ai (affiliate)
• Wan 2.7 Guide — Complete overview of Wan AI features and capabilities
• Wan Image-to-Video Generator — Animate still images with Wan AI
• Alibaba Tongyi Lab Research — Official research page for Wan AI model development
• Wan 2.7 vs Kling Comparison — Related reading on how Wan compares to other AI video models