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Devin Zhang
Devin Zhang

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How to Build a Simple AI Video Generation Workflow

#ai

AI video generation is changing how developers and creators produce visual content. Instead of building every scene manually, we can now generate short videos using text prompts and reference images.

But getting consistent results requires more than typing a simple description.

In this tutorial, we'll explore a practical AI video generation workflow using structured prompts, Python, and tools like Seedance 2.5.

1. Structure Your Video Prompts

A vague prompt such as:

Create a cinematic video of a futuristic city.
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gives the AI too much freedom.

A better approach is to specify the subject, environment, action, camera movement, and visual style.

Subject: Futuristic city at night
Environment: Neon-lit streets with skyscrapers
Action: Flying vehicles moving between buildings
Camera: Slow forward tracking shot
Lighting: Blue and purple neon reflections
Style: Photorealistic, cinematic
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Structured prompts make results easier to reproduce and improve.

2. Generate Videos with Seedance 2.5

Seedance 2.5 is an AI video generation platform designed to turn creative instructions and reference materials into videos.

A basic workflow looks like this:

Text Prompt + Reference Images
              |
              v
        Seedance 2.5
              |
              v
       Generated Video
              |
              v
       Review & Export
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Start with a clear prompt, add relevant visual references, and select appropriate generation settings.

For example, to create a product video:

Create a cinematic product showcase of
wireless headphones on a dark platform.

Use soft studio lighting and a slow
360-degree camera movement.

Maintain consistent product proportions
and a clean minimalist background.
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Reference images can help communicate product details that are difficult to describe using text alone.

3. Automate Prompt Creation with Python

If you're generating multiple videos, manually writing prompts becomes repetitive.

Python can simplify the process:

def create_prompt(subject, action, camera, style):
    return f"""
    Subject: {subject}
    Action: {action}
    Camera: {camera}
    Style: {style}
    """.strip()


prompt = create_prompt(
    subject="A futuristic electric car",
    action="Driving through a neon city",
    camera="Smooth tracking shot",
    style="Cinematic, photorealistic"
)

print(prompt)
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You can use the generated prompt in Seedance 2.5 or another compatible video generation tool.

This approach is useful for creating repeatable workflows, especially when working with multiple scenes or product variations.

4. Improve Your Results

A few practical tips can make a noticeable difference:

  • Be specific: Describe the subject, movement, and environment clearly.
  • Use references: Provide images when visual consistency matters.
  • Keep actions simple: Avoid too many camera movements in one short clip.
  • Iterate systematically: Change one prompt element at a time.

You can also use FFmpeg to convert generated videos into widely compatible MP4 files:

ffmpeg -i generated.mp4 \
  -c:v libx264 -crf 20 \
  -pix_fmt yuv420p \
  output.mp4
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Conclusion

AI video generation becomes much more useful when treated as a repeatable workflow rather than a one-time experiment.

By combining structured prompts, reference images, and simple Python automation, developers can create more predictable results.

Platforms like Seedance 2.5 make it possible to experiment with AI-assisted video creation, while tools like Python and FFmpeg help organize the production process.

The key is simple: better inputs lead to more controllable outputs.

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