How to Think Like a Filmmaker When You Make AI Video with Seedance

# Cheap — And How to Stop That

# A Practical, Repeatable Workflow Follow these staged steps to produce AI video that reads like planned filmmaking instead of random clips.

1. Develop a clear concept

A concept is the story you want the viewer to understand. It can be simple: place a product in an unexpected environment, or explain an idea with a visual metaphor. The point is to know the goal before you open any tools. Use an LLM to expand a basic idea into a short sequence of beats and variations so you have a narrative map to follow.

2. Build key visuals first

Design anchor images that define how the piece will look. Ross Symons uses a three-part framework: the subject (hero), the environment, and a secondary character or element. Create a mock product or hero image in a still-image tool (Midjourney, Gemini, or similar). Establish composition, lighting, and style at the image level—these choices carry into the video.

3. Write prompts for diffusion models the right way

Diffusion models respond to structured keyword prompts, not conversational sentences. If you prefer writing descriptions in plain language, ask an LLM to convert your description into the model-specific syntax (for example, Midjourney-format prompts). That conversion produces cleaner, more consistent visuals.

4. Generate clips with Seedance

Once you have clear key visuals and structured prompts, feed those assets and sequences into Seedance. Use image references and the narrative beats you developed to guide clip generation. Keep each generated clip focused on a single action or camera move so you can assemble them like shots in a film edit.

5. Assemble and refine

Edit generated clips into a sequence that follows your concept beats. Because you designed the visuals and narrative first, you'll have clearer choices about pacing, transitions, and sound design. Iterate: adjust prompts for clips that don't match the established look, or regenerate specific frames rather than redoing entire scenes.

# Real examples (how the steps map to work)

# Practical tips

• Master still-image prompting before moving to video. The same visual-direction principles apply to both.

• Use LLMs to translate plain-language descriptions into the keyword-based prompt format needed by diffusion models.

# Bottom line Treat AI video like filmmaking: start with intention, design anchor visuals, use the right prompt structure for diffusion models, then generate and edit clips deliberately. That workflow produces much more cinematic, repeatable results than ad-hoc prompting.