PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts: Kamrul Agarwal

How I Prototype Game Trailers with Seedance 2.5 Before Writing a Single Line of Code

Kamrul Agarwal — Thu, 25 Jun 2026 06:17:34 +0000

When I start a new game project, I usually have dozens of ideas.

The problem isn't creativity.

It's figuring out whether those ideas actually feel exciting when they become visual.

Building a playable prototype takes weeks.

Creating a polished trailer takes even longer.

I wanted a faster way to test the atmosphere of a game before investing months in development.

That's why I started experimenting with AI video workflows.

What

Recently I've been using Seedance 2.5 as a pre-production tool instead of a final production tool.

Rather than generating random cinematic clips, I use it to visualize game worlds, opening sequences, and cutscene ideas.

Some features that fit this workflow include:

Text-to-video generation
Image-to-video generation
Multi-reference support
Native audio generation
Multi-shot scene creation

Seedance 2.5 expands on earlier versions with longer native clips, more reference inputs, and improved prompt adherence, making it more practical for structured storytelling workflows.

How

Step 1: Write the Scene

Instead of writing a full game script, I begin with one moment.

Example:

A lone explorer enters an abandoned underground laboratory.

Step 2: Collect References

I prepare:

character concept art
environment sketches
color palette
mood references

Using multiple references helps keep the world visually consistent throughout the sequence. Seedance 2.5 supports a much larger reference budget than previous versions, making it easier to organize characters, locations, and visual styles in one project.

Step 3: Create a Shot List

Rather than using one giant prompt, I separate the scene into shots.

Shot 1

Wide establishing shot.

Shot 2

Character enters the room.

Shot 3

Camera reveals an abandoned experiment.

This makes it easier to review pacing before gameplay even exists.

Step 4: Iterate

If one shot doesn't work, I adjust only that part instead of rebuilding the entire sequence.

Example Prompt

cinematic underground laboratory, slow tracking camera, flickering emergency lights, abandoned research equipment, mysterious atmosphere, realistic cinematic motion

Use Case

This workflow works well for:

Indie Game Developers

Visualizing story scenes before production.

Game Designers

Testing environmental storytelling.

Narrative Designers

Planning opening cinematics.

Kickstarter Creators

Creating teaser videos for campaigns.

Small Studios

Pitching game ideas without building a full demo.

Why

After several experiments, I noticed a few practical advantages:

Faster idea validation
Better communication with collaborators
Easier storyboard creation
More consistent visual planning
Less time spent creating placeholder cinematics

The biggest improvement wasn't rendering quality.

It was being able to answer a simple question early:

"Does this game world feel interesting enough to build?"

For me, that saved far more time than any rendering shortcut.

Final Thoughts

I still rely on traditional game engines for final cinematics.

But during pre-production, Seedance 2.5 has become a useful brainstorming tool.

Instead of waiting until the game is playable, I can quickly explore mood, pacing, and visual storytelling from a simple concept.

If you're developing a game, try creating one cinematic scene before writing all the gameplay systems.

Sometimes seeing the world in motion is the fastest way to decide whether an idea is worth pursuing.

How I Started Creating Recruitment Videos with Wan 2.7

Kamrul Agarwal — Wed, 24 Jun 2026 01:50:35 +0000

Hiring has changed a lot over the past few years.

Job descriptions are still important, but many candidates decide whether to apply after watching a short video about the company.

The problem?

Creating a recruitment video for every position isn't realistic.

It usually means:

scheduling video shoots
editing multiple versions
updating content whenever a role changes

I wanted a faster workflow for creating simple recruitment videos.

That's when I started experimenting with AI video generation.

What

Recently I started using Wan 2.7 to build short recruitment videos from existing company assets.

Instead of filming everything from scratch, I combine office photos, product screenshots, and simple prompts.

The workflow supports:

Text-to-video
Image-to-video
Reference-based generation
Instruction-based editing
Video extension

One thing I like is that Wan 2.7 combines generation, editing, and reference control into a single workflow instead of treating them as separate tools.

How

Step 1: Collect Existing Materials

I prepare:

office photos
team activity images
product screenshots
company logo

Most companies already have these assets.

Step 2: Plan the Story

Instead of writing one long prompt, I divide the video into several scenes.

Scene 1
Company introduction

Scene 2
Working environment

Scene 3
Team collaboration

Scene 4
Open positions

Breaking the story into scenes usually creates a more natural sequence than one large prompt.

Step 3: Write Scene-Based Prompts

Example:

modern technology office, employees collaborating around a meeting table, natural daylight, slow camera movement, professional documentary style

I avoid adding too many style keywords.

Keeping the prompt focused gives more consistent results.

Step 4: Review and Refine

If one scene doesn't feel right, I edit only that section instead of rebuilding the whole video.

Instruction-based editing makes small revisions much easier than restarting from scratch.

Use Case

This workflow works well for:

HR Teams

Creating recruitment videos.

Startup Founders

Introducing company culture.

Tech Companies

Explaining engineering roles.

Universities

Promoting internship programs.

Remote Teams

Showing distributed work environments.

Why

After several hiring campaigns, I noticed a few practical benefits:

Faster content production
Easier updates when job openings change
Better engagement than static job posts
More efficient use of existing company assets
Consistent visual style across recruitment campaigns

The biggest benefit wasn't making cinematic videos.

It was helping candidates understand the company before the first interview.

Final Thoughts

I still use professionally filmed videos for employer branding campaigns.

But for everyday hiring, Wan 2.7 has become a practical way to turn existing photos and ideas into short recruitment videos.

If your team is hiring regularly, try creating one scene at a time instead of producing a long promotional video all at once.

You may discover that a simple, structured workflow is easier to maintain—and much easier to update as your company grows.

How I Use Grok Imagine 1.5 to Explore UI Concepts Before Opening Figma

Kamrul Agarwal — Wed, 17 Jun 2026 02:33:48 +0000

One thing that slowed down my design process wasn't drawing interfaces.

It was deciding which direction to explore.

Before opening Figma, I'd often spend 30–40 minutes looking for inspiration, collecting screenshots, and sketching rough layouts.

Most of those ideas never made it into the final design.

I wanted a faster way to visualize concepts before committing to a full UI.

That's when I started experimenting with AI image workflows.

What

Recently I've been using Grok Imagine 1.5 as an idea-generation tool rather than a finished design tool.

Instead of asking it to create polished mockups, I use it to explore visual directions.

Some capabilities that fit this workflow include:

Text-to-image generation
Style exploration
Reference-based editing
Multi-image refinement
Image-to-video support for later iterations

Grok Imagine is designed to support an end-to-end visual workflow, from image generation and editing to animation.

How

Step 1: Describe the Product

Instead of listing UI components, I describe the product and the user experience.

Example:

A modern fitness app focused on simplicity, soft gradients, rounded cards, clean typography, and a calming color palette.

Step 2: Describe the Mood

This changed my results more than adding extra keywords.

Example:

bright morning light, minimal workspace, premium mobile app aesthetic, subtle shadows, Scandinavian design inspiration

Prompt guides for Grok Imagine recommend writing prompts as natural scene descriptions that include mood, lighting, and context rather than keyword lists.

Step 3: Generate Several Directions

Instead of searching for one perfect image, I usually create:

minimalist version
dark mode version
colorful startup style
premium enterprise style

This gives me multiple directions before I begin designing.

Step 4: Rebuild in Figma

Once I find a visual direction I like, I recreate the layout manually in Figma.

The AI output isn't the final design.

It's the creative starting point.

Example Prompt

modern mobile banking app dashboard, clean card layout, soft blue gradient, rounded interface, premium fintech branding, realistic smartphone presentation

Use Case

This workflow has been useful for:

UI/UX Designers

Exploring layout concepts quickly.

Startup Founders

Visualizing MVP ideas before hiring a designer.

Product Teams

Creating mood boards for discussions.

Freelancers

Presenting multiple design directions to clients.

App Developers

Planning interfaces before implementation.

Why

After several projects, I noticed a few improvements:

Less time searching for inspiration
Faster concept validation
More creative variations
Better communication with clients
Smoother transition into Figma

The biggest improvement wasn't image quality.

It was reducing the time spent deciding where to start.

Final Thoughts

I don't use AI to replace interface design.

I use it to remove the blank-page problem.

If you're designing apps, dashboards, or web products, try generating a few visual concepts before opening your design tool.

You might spend less time searching for inspiration and more time refining ideas that already have a clear direction.

How I Create Product Launch Teasers with Kling 3.0

Kamrul Agarwal — Thu, 11 Jun 2026 02:39:32 +0000

Hook

Whenever we launch a new product, there's always pressure to create video content quickly.

The challenge isn't getting ideas.

The challenge is producing enough visual content before launch day.

Traditional video production takes time:

shooting footage
editing clips
creating animations
exporting multiple formats

For small teams, that's often the biggest bottleneck.

That's why I started testing AI video workflows.

What

Recently I've been experimenting with Kling 3.0 for creating product teaser videos.

Instead of replacing our normal workflow, I use it during the early content planning stage.

A few features I found useful:

Text-to-video generation
Image-to-video workflows
Cinematic camera movement
Multi-scene generation
Social-media-friendly formats

The goal isn't creating a final commercial.

It's creating launch assets faster.

How

Step 1: Prepare a Product Image

I usually start with:

a product render
a website mockup
a packaging image

One strong image is enough.

Step 2: Create a Teaser Prompt

Instead of describing the product itself, I focus on presentation.

Example:

premium product reveal, slow rotating camera movement, dramatic lighting, dark background, cinematic commercial style

This immediately produces more usable results.

Step 3: Generate Multiple Angles

I typically test:

close-up reveal
side profile
rotating showcase
dramatic zoom-in

This gives me several options for social content.

Step 4: Export Short Clips

Most of the time I use clips for:

Twitter/X posts
LinkedIn announcements
landing page headers
Instagram Reels
YouTube Shorts

Prompt Example

cinematic product reveal, premium wireless headphones on reflective surface, slow orbit camera movement, studio lighting, luxury advertising style, realistic reflections

Use Case

This workflow works well for:

SaaS Founders

Creating feature launch videos.

E-commerce Brands

Generating product showcases.

Marketing Teams

Building teaser campaigns.

Content Creators

Creating promotional visuals.

Startup Founders

Testing launch concepts quickly.

Why

After several launch cycles, I noticed a few advantages:

Faster content production
More creative variations
Less dependency on video editing
Easier experimentation
Faster campaign preparation

The biggest improvement wasn't video quality.

It was speed.

Instead of spending half a day creating one concept, I could test multiple ideas in less than an hour.

Final Thoughts

I still use traditional editing tools for final campaigns.

But Kling 3.0 has become a useful part of the ideation process, especially when I need several visual concepts before a launch.

If you're creating marketing content, product announcements, or teaser campaigns, try building a simple AI video workflow before jumping into production.

You might discover ideas that wouldn't have appeared in a traditional editing process.

Explore the workflow yourself and see how it fits your content creation process.

How I Create YouTube Intro Videos with LTX 2.3

Kamrul Agarwal — Mon, 08 Jun 2026 09:26:33 +0000

I run a small YouTube channel, and one thing I always struggled with was creating intro sequences.

Not full videos.

Just those first few seconds that make people stop scrolling.

Recently I started experimenting with LTX 2.3, and it became part of my content workflow much faster than I expected.

My Goal

I wanted to create:

channel intros
cinematic opening shots
transition scenes
visual hooks for Shorts

without spending hours editing.

My Workflow

Step 1: Start With One Image

Usually I generate or choose a single frame that matches the video's theme.

For example:

a city skyline
a gaming setup
a futuristic room
a product shot

Step 2: Focus on Motion

Instead of describing every detail, I focus on movement.

Example:

slow dolly-in camera, neon reflections, atmospheric fog, cinematic lighting, subtle motion

LTX's prompting guide recommends emphasizing shot design, action, camera movement, lighting, and atmosphere rather than keyword-heavy prompts.

Step 3: Build a Short Sequence

I usually generate several versions:

slow push-in
orbit movement
handheld movement
static cinematic shot

Then I pick the strongest one.

What Improved My Results

After dozens of tests, I found:

camera descriptions matter more than visual adjectives
one clear action works better than multiple actions
shorter prompts produce more stable motion
image-to-video workflows outperform random text-only generations

The LTX team also recommends keeping prompts focused and maintaining one main action for every few seconds of video.

Example Prompt

One of my favorite intro prompts:

cinematic aerial shot of a futuristic city at sunrise, slow camera push forward, warm orange lighting, atmospheric fog, realistic motion, film trailer aesthetic

Simple.

But surprisingly effective.

Final Thoughts

I still edit my final videos manually.

But for:

YouTube intros
Shorts hooks
trailer openings
visual transitions
channel branding

LTX 2.3 has become one of the fastest tools I've found for generating cinematic opening sequences.

Curious how other creators are using AI video tools in their editing workflow.

I’ve Been Using ERNIE Image for Text-Heavy AI Designs

Kamrul Agarwal — Tue, 12 May 2026 02:03:16 +0000

Recently I started experimenting with ERNIE Image for posters, cinematic thumbnails, and AI-generated design layouts.

One thing that immediately stood out was the text rendering quality. Most image models still struggle with readable typography inside images, but ERNIE Image seems much better at handling structured layouts and bilingual prompts.

What I Tested
I tried generating:
movie-style posters
YouTube thumbnails
cyberpunk city scenes
manga/comic layouts
social graphics with embedded text

The strongest outputs came from prompts with:
clear composition instructions
lighting direction
typography placement
cinematic camera descriptions
Instead of writing:
“futuristic poster”
I used prompts like:

cinematic sci-fi movie poster, neon blue lighting, detailed city background, bold readable title text, dramatic composition, high contrast, realistic reflections

The results felt much more production-ready.

What Helped Most

A few things improved the outputs consistently:
shorter structured prompts
explicit layout instructions
camera angle references
readable text requests
separating subject and background descriptions

ERNIE Image also seems especially strong for poster-style visuals and design-heavy generations compared to many standard diffusion models.

Final Thoughts

Still testing different workflows, but ERNIE Image feels surprisingly good for creators working on AI posters, storyboards, thumbnails, and cinematic visual concepts.

Curious whether other people are using cinematic prompts or layout-focused prompts for AI image generation lately.

I Tested HappyHorse 1.0 for AI Storytelling Videos

Kamrul Agarwal — Tue, 12 May 2026 01:18:26 +0000

Recently I’ve been experimenting with HappyHorse 1.0 for cinematic AI video generation and short dialogue scenes.

What caught my attention first was the built-in audio and lip-sync workflow. Most AI video tools still require separate audio pipelines, but HappyHorse 1.0 focuses heavily on synchronized video + sound generation.

What I Tested
I tried generating:
dialogue-driven scenes
cinematic close-ups
neon sci-fi environments
AI trailer shots
character storytelling clips

The strongest results came from prompts with clear camera instructions and mood descriptions.

Instead of writing:
“man talking in cafe”
I used prompts like:
cinematic medium close-up, warm cafe lighting, natural facial movement, soft camera push-in, realistic dialogue pacing, ambient coffee shop sound

The motion and lip-sync looked much more natural.

What Helped Most

A few things noticeably improved the outputs:

shorter cinematic prompts
explicit camera movement
consistent character descriptions
mood + lighting details
dialogue pacing hints

HappyHorse 1.0 also seems particularly strong for talking scenes and short storytelling clips compared to many general AI video models.

Final Thoughts

Still testing different workflows, but HappyHorse 1.0 feels promising for creators working on AI trailers, talking-character content, and cinematic social videos.

Curious whether other people are using cinematic prompts or more minimal prompts for AI video lately.

Why I Started Using GPT Image 2 for Concept Art

Kamrul Agarwal — Tue, 12 May 2026 01:15:55 +0000

Lately I’ve been experimenting with GPT Image 2 for concept art, thumbnails, and cinematic scene generation.

What impressed me most is how well it handles detailed instructions and in-image text compared to older image models. A lot of AI image tools still struggle with composition consistency, but GPT Image 2 feels much better when prompts are structured clearly.

Prompt Style That Worked Best
Instead of writing vague prompts like:
“futuristic city”
I started using more structured cinematic descriptions:

wide-angle cinematic shot, futuristic Tokyo alley, neon reflections on wet pavement, soft fog, realistic lighting, shallow depth of field, photorealistic

The results became noticeably more consistent.
What Helped the Most
A few things improved my outputs immediately:
shorter prompts with clear structure
explicit lighting descriptions
camera angle references
material and texture details
keeping the subject description consistent

GPT Image 2 seems to reward clarity more than “fancy wording.

Final Thoughts
Still testing different workflows, but GPT Image 2 has become one of my favorite tools for fast visual ideation and AI-assisted design work.

Curious how other people are structuring prompts for cinematic or photorealistic results lately.

My AI Video Workflow with Wan 2.7

Kamrul Agarwal — Mon, 11 May 2026 08:54:20 +0000

Recently I’ve been experimenting with Wan 2.7 AI Video Generator for cinematic AI video creation, especially short-form storytelling and motion-heavy scenes.

What surprised me most wasn’t just the visual quality — it was the motion consistency between shots.

What I Tested

I tried generating:

cinematic walking scenes
slow camera pushes
dialogue-style character shots
neon city environments
anime-inspired motion sequences

Most AI video models still struggle with camera coherence and subject stability, but Wan 2.7 handled some of these prompts better than I expected.

Prompt Structure That Worked Best

What gave me the best results was using:

clear camera movement
lighting direction
scene mood
subject action
pacing description

Instead of:

“girl walking in city”

I used prompts like:

cinematic medium shot, slow dolly movement, neon reflections on wet street, female character walking naturally, nighttime atmosphere, realistic motion blur

The outputs felt much more film-like.

What I Learned

A few things helped a lot:

shorter prompts performed better
cinematic language improved motion
scene pacing mattered more than detail overload
consistent character descriptions helped shot continuity
Final Thoughts

Still testing different workflows, but Wan 2.7 feels promising for creators experimenting with AI short films, trailers, and visual storytelling.

Curious what prompting styles others are using for AI video lately.