DEV Community: AI SHU

A Practical Checklist for Writing AI Video Prompts That Keep Products Stable

AI SHU — Thu, 28 May 2026 12:08:09 +0000

AI video prompting has a common failure pattern: the first result looks exciting, but the product changes shape, the camera moves too aggressively, the label area drifts, or the scene becomes too busy to use in an ad.

That usually happens because the prompt is trying to do too many jobs at once. It describes the product, the scene, the lighting, the camera, the emotion, the motion, the style, and the final commercial purpose all in one loose paragraph. The model follows parts of it, ignores parts of it, and invents the rest.

A better approach is to write prompts like production notes. The goal is not to sound poetic. The goal is to make a short clip that can survive review: the product is recognizable, the motion supports the selling idea, and the output can be edited into a real campaign.

This checklist is designed for product ads, ecommerce clips, creator videos, and Seedance-style image-to-video workflows.

The core principle: separate identity from motion

A product video prompt has two different responsibilities.

Identity is what must stay stable:

product shape
color
packaging
label area
material finish
camera composition
person or character identity
background logic

Motion is what should change:

camera movement
product reveal
lighting change
environmental motion
human gesture
transition
timing
atmosphere

Weak prompts mix these together. Strong prompts define both clearly.

A reliable structure looks like this:

Animate the existing scene.
Product identity: what must remain stable.
Primary motion: the main movement.
Camera: how the camera moves.
Atmosphere: secondary motion or lighting.
Commercial goal: what the clip should communicate.
Constraints: what to avoid.

That structure works because it tells the model what to protect and what to animate.

Checklist item 1: start with one commercial job

Before writing the prompt, decide what the video should accomplish.

Do you want the clip to:

introduce a product?
show a benefit?
create a premium mood?
demonstrate a before-and-after?
reveal a texture?
make a landing page feel more alive?

If the job is unclear, the motion will usually be unclear too.

For example, this is vague:

Make a cinematic product video with beautiful lighting and smooth motion.

This is clearer:

Create a premium hero reveal for a new skincare bottle. The clip should make the product feel clean, fresh, and high-end in the first two seconds.

The second version gives the model a commercial direction.

Checklist item 2: define the product preservation rules

Most product video failures are identity failures. The object looks different after motion starts.

Add preservation rules directly:

Keep the bottle shape, cap, label area, glass material, gold tint, and product position stable throughout the clip.

For a fashion product:

Keep the shoe silhouette, sole shape, fabric texture, color blocking, and logo-free design consistent.

For a device:

Keep the device proportions, screen position, button placement, metallic finish, and edge geometry unchanged.

Do not assume the model knows which details matter. Tell it.

Checklist item 3: use one primary motion

Many prompts fail because they ask for five movements at once.

This is risky:

The product rotates, water splashes, light sweeps across it, the camera orbits, the background changes, and particles move dramatically.

Start simpler:

Primary motion: a slow camera push-in while a soft light sweep moves across the bottle.

One primary motion plus one supporting motion is usually enough for a short product ad. If the result is stable, create more ambitious variations later.

Checklist item 4: specify camera behavior

Camera movement is one of the strongest levers in AI video. It also causes many artifacts.

Use clear camera language:

static camera
slow push-in
gentle orbit
macro slide
top-down hold
slight handheld drift
locked product framing

For product stability, "slow" and "gentle" matter. A fast orbit can make packaging warp. A dramatic zoom can break proportions. A locked or subtle camera move is often more commercial than an aggressive cinematic move.

Example:

Camera: slow push-in from a stable front three-quarter angle. Keep the product centered and avoid rotation beyond a few degrees.

Checklist item 5: keep atmosphere secondary

Atmosphere helps a video feel premium, but it should not overpower the product.

Good supporting motion:

soft light sweep
gentle fabric movement
small water droplets
subtle steam
slow shadow movement
clean reflection shift
minor background blur

Risky supporting motion:

heavy splashes
explosive particles
crowded animated props
fast background transitions
complex hand interactions
text appearing inside the scene

For ads, the product should win the frame.

Checklist item 6: write constraints in plain language

Constraints are not magic, but they help.

Useful constraints:

Avoid product warping, label distortion, extra objects, unreadable text, sudden camera shake, changing bottle shape, and busy background motion.

For a person:

Preserve the same face, outfit, body proportions, and pose direction. Avoid face morphing, extra fingers, and unnatural gestures.

For an app or UI:

Keep the interface layout stable. Avoid changing screen structure or inventing readable text.

The constraint should match the risk of the scene.

A before-and-after prompt example

Weak prompt:

Make a beautiful cinematic video of this skincare product with water, light, premium mood, and social media ad style.

Stronger prompt:

Animate the existing skincare product photo as a short premium beauty ad.
Product identity: keep the bottle shape, cap, gold liquid color, label area, and stone surface stable.
Primary motion: slow camera push-in toward the product.
Atmosphere: soft morning light moves across the background, with a few subtle water droplets near the base.
Commercial goal: make the product feel fresh, clean, and high-end in the first two seconds.
Constraints: avoid product warping, label distortion, heavy splashes, extra bottles, text overlays, or fast camera movement.

The second prompt is not longer for the sake of length. It is more organized.

When to use a dedicated workflow tool

If you are testing multiple product video directions, it helps to keep prompts, source images, and variations in one place. A tool like C Dance AI can support Seedance-style image-to-video testing when creators want to compare different motion prompts against the same source image.

The workflow matters more than the tool name. Keep the source image stable, change one motion variable at a time, and compare results with a checklist.

A simple scoring system

After generating a clip, score it from 1 to 5 on each dimension:

Dimension	What 5 means
Product stability	Product stays recognizable and consistent
Motion clarity	Movement supports the ad idea
First-second hook	Viewer understands where to look immediately
Mobile readability	Product remains clear when viewed small
Edit readiness	Clip can be trimmed, captioned, or placed in an ad

Do not keep a clip just because it looks impressive. Keep the clip that can actually do campaign work.

Final takeaway

Good AI video prompting is less about writing dramatic descriptions and more about making controlled production decisions.

Define the commercial job. Protect the product identity. Use one primary motion. Keep camera behavior simple. Add atmosphere carefully. Write constraints that match the actual risks.

That is how AI video prompts become more than experiments. They become repeatable creative assets.

An AI Image Prompt Debugging Checklist for Better Outputs

AI SHU — Thu, 28 May 2026 12:00:27 +0000

When an AI image prompt fails, the natural reaction is to add more words. Sometimes that helps. Often it makes the prompt noisier.

A better approach is to debug the output by category. What actually failed: product identity, composition, lighting, style, use case, or constraints?

This checklist helps you revise prompts systematically.

Problem 1: the product changes shape

Fix the identity layer.

Add:

Keep the same product shape, proportions, material finish, color, cap, label area, and edge geometry.

If the product is still unstable, create a reference sheet before generating campaign images.

Problem 2: the image looks good but is not usable

Fix the purpose layer.

Add where the image will be used:

Commercial purpose: landing page hero with clean negative space on the right.

Or:

Commercial purpose: ecommerce detail image focused on product texture.

Usability improves when the model knows the job.

Problem 3: the frame is cluttered

Fix composition.

Use one main product, simple background, clean foreground, limited props, and mobile-safe crop.

Remove unnecessary adjectives and extra objects.

Problem 4: lighting does not match the brand

Replace vague style words with specific lighting.

Instead of:

Premium cinematic lighting.

Use:

Soft morning window light, warm stone surface, subtle natural shadows, calm skincare campaign mood.

Specific lighting is easier to reproduce.

Problem 5: text is messy

Do not force exact text into the image. Add it later in design software.

Prompt:

No readable text, no logos, no fake words, no UI brand names.

Problem 6: variations feel unrelated

Create a shared identity block and reuse it.

Product identity: matte sage green cylindrical pump bottle, white pump, smooth label area, soft-touch finish, minimal premium packaging.

Then change only the scene or composition.

Workflow tools like GPTImg2 can help keep prompt variations and output directions organized while testing GPT Image 2 images.

Debugging template

What failed: [identity / composition / lighting / purpose / constraints]
Keep: [parts of the output that worked]
Change: [one clear revision]
Avoid: [specific failure]

Example:

Keep the soft morning lighting and stone surface.
Change the product to a larger front three-quarter view.
Avoid extra bottles, readable text, and distorted pump shape.

Debug one layer at a time

The fastest way to make prompts worse is to change everything after one failed output. If the product shape, background, lighting, and camera all change in the next version, you will not know which fix helped.

Use a controlled revision process:

Keep the strongest part of the image.
Identify the main failure.
Revise one prompt layer.
Generate a new version.
Compare against the original.

Example:

Failure	Do not change yet	Change first
Product too small	lighting, environment	composition
Product shape wrong	background, mood	identity
Too cluttered	product details	props and framing
Off-brand lighting	product structure	light direction
Bad text	whole image	text constraint

This approach is slower than rewriting emotionally, but it produces better learning.

Create a reusable identity block

For product work, write an identity block once and reuse it.

Product identity: tall rectangular amber glass perfume bottle, ribbed vertical texture, minimal gold cap, transparent warm liquid, clean label area with no readable text.

Then only change the campaign layer:

Campaign version: landing page hero with negative space.

or:

Campaign version: macro detail shot focused on ribbed glass texture.

This keeps the product from drifting between images.

What to save after a good output

When an output works, save more than the image. Save:

the full prompt
the identity block
the use case
the failed versions
the final image
the reason it worked

This turns successful prompts into a repeatable visual system.

Build a prompt checklist before generating

Before running the next version, check whether the prompt includes:

product identity
commercial purpose
composition
lighting direction
constraints
crop or layout need
what should not change

If one of these is missing, add it before generating. A prompt that says "premium product photo" might become:

Commercial purpose: landing page hero. Composition: product on the left, clean negative space on the right. Keep product shape, color, cap, and material finish stable. No readable text or extra products.

That is not just longer. It gives the image a job and protects the details that matter.

If an editor asks for a practical example, show one before-and-after prompt. That makes the article feel instructional instead of promotional, and it gives readers a pattern they can reuse immediately.

Final takeaway

Do not fix every failed AI image by making the prompt longer.

Identify the failure category, revise one layer, and test again. Better prompts come from diagnosis, not just more adjectives.

Why Happy Horse Became the King of AI Video? Its Legendary Rise Explained

AI SHU — Wed, 15 Apr 2026 11:14:55 +0000

If you follow the global technology sector, you have undoubtedly seen one name dominate headlines recently: Happy Horse. Emerging seemingly out of nowhere as a formidable dark horse on April 7, 2026, this cutting-edge AI video model achieved a decisive victory to claim the top position on the authoritative global AI Video Arena leaderboard, dethroning the long-reigning champion, Seedance 2.0

Beyond its breathtaking technical capabilities, Happy Horse has fundamentally rewritten the rulebook for the AI video industry with its revolutionary promise: double the performance at half the cost. This analysis reveals the strategic and technical secrets behind Happy Horse's unprecedented ascent to the pinnacle of global AI video innovation.

I. Who Is Happy Horse?
Happy Horse s not a product of secretive offshore labs or foreign tech conglomerates. It is a fully indigenous innovation developed by the Future Life Lab at Alibaba's Taotian Group in China. Spearheaded by Zhang Di, former Vice President of Kuaishou and a leading figure in AI video technology, the team engineered this technological tour de force in just one year.

Its core mission is elegantly simple: to make professional AI video generation realistic, seamless, user-friendly, and universally affordable.

The platform is officially available at: https://happy-horse.pro

II. Why It Dominates the Rankings: Three Unmatched Competitive Advantages

Visual Fidelity: A Benchmark for Hyper-Realistic Detail
Generating a video scene such as "walking in the rain" highlights the stark contrast between Happy Horse and its competitors. While conventional models produce rigid movements and low-resolution effects, Happy Horse delivers cinematic precision:
12.7% enhancement in motion coherence: Fluid, lifelike human movements free of stuttering or glitches.
9.4% improvement in detail resolution: Crystal-clear raindrop reflections, dynamic hair simulation, fabric textures, and reflective surface rendering.
15.2% boost in natural motion dynamics: Fluid, biomechanically accurate movements for activities such as dancing or sports, eliminating the "artificial stiffness" prevalent in older models.
Technical Edge: It employs an advanced "truth-from-noise" denoising paradigm, achieving perfect detail in just 8 iterative steps—a drastic improvement over the industry standard of 20–50 steps.
Precision Audiovisual Synchronization
A critical flaw in most AI video platforms is disjointed audio and visuals. Happy Horse solves this through a unified multimodal architecture:
Dynamic scene generation where sound effects, music, and ambient noise are perfectly synchronized with visual elements.
Industry-leading lip-sync technology, aligning virtual character speech with audio within a margin of error of less than one frame.
This breakthrough eliminates the "out-of-sync" problem plaguing platforms like Seedance 2.0
Cost-Efficiency Leader: Double Performance, Half the Cost
Happy Horse stands as a powerful market disrupter:
Its operational cost is approximately 50% of that of the industry benchmark, Seedance 2.0.
It supports flexible open-source deployment and enterprise customization, avoiding restrictive platform lock-in.
This combination of superior quality and affordability has triggered a mass migration of creators, studios, and advertisers to the Happy Horse platform

III. Definitive Performance Metrics & Industry Milestones

Achieved a historic Elo score of 1,332 to top the global AI Video Arena rankings, surpassing Seedance 2.0 by a significant margin.
Secured first place in both text-to-video and image-to-video categories, ranking #2 globally in audio-integrated video generation.
Established technological leadership for China in the global AI video race, breaking long-standing Western dominance in core generative AI technologies.

IV. Transformative Real-World Applications
Short-Content Creation: Enables instant video generation from text prompts, reducing production costs by up to 70%.
AI-Driven Entertainment: Powers scalable production of short dramas and animated content, accelerating digital IP adaptation.
Programmatic Advertising: Facilitates rapid creation of multilingual, high-conversion dynamic marketing videos.
Immersive Education: Delivers interactive historical and educational simulations for enhanced learning experiences.

V. Market Impact: A Paradigm Shift in Global AI
The launch of Happy Horse has sent reverberations across global markets:
A nearly 8% surge in Alibaba's stock price, restoring investor confidence in applied AI leaders.
Explosive growth in AI infrastructure and computing supply chains.
The industry's transition into the Post Sora Era, where competition is defined by physical realism, temporal consistency, and economic accessibility.

Conclusion: The New Monarch of AI Video
Happy Horse represents more than a technological upgrade—it is a paradigm shift. By mastering visual quality, motion realism, and audiovisual synchronization while slashing costs, it has redefined the global standard for generative video AI.

While competitors focus on isolated innovations, Happy Horse delivers a complete, production-grade solution. It transforms AI video from an experimental novelty into an essential, accessible productivity tool for individual creators and global enterprises alike.
The revolution in visual content creation has arrived—and its name is Happy Horse.

The model informationis currently available at https://happy-horse.pro. The full leaderboard methodology can be reviewed on the Artificial Analysis Video Arena. Rankings described in this article reflect the public state of the leaderboard as of April 8, 2026.

Happy Horse is a pioneering AI video generation platform dedicated to pushing the boundaries of artificial intelligence and creative content production. Backed by a seasoned team of AI researchers and engineers with decades of collective experience in computer vision and multimedia processing, Happy Horse is committed to delivering cutting-edge technology that empowers creators, enterprises, and developers worldwide.

The platform specializes in high-fidelity text-to-video, image-to-video, and reference animation technologies, focusing on physical realism, cross-scene consistency, and cinematic audiovisual synchronization. Headquartered in Hong Kong, Happy Horse aims to redefine the future of digital content creation by making state-of-the-art AI video production accessible to all.

For more information about Happy Horse 1.0, visit https://happy-horse.pro or contact support@happy-horse.pro.

What a divided world.

AI SHU — Wed, 04 Mar 2026 12:11:18 +0000

On one side, the AI scene is booming like crazy—from last year’s Nano Banana (https://nanabanana2.pro), to Seedance (https://cdance.net) over the Spring Festival, the shockwave of Sora (https://sora-image.com), and today’s news about a top figure leaving Qwen (https://qwenimg2.com).
On the other side, wars rage on without end.
The world is changing at a dizzying pace, yet I’m stuck in trivial daily routines: work, my kid’s studies, and all the little things. I can only keep polishing these trivial lines of code in my fragmented spare time.