Structured Prompts: The Director's Manual for AI Video Generation
Video generation quality now hinges on prompt structure rather than model capability alone; developers who adopt frameworks like SCELA+ and its negation-avoidance rules can produce usable footage from models like Seedance, while those who keep writing vague descriptions will keep getting plastic-looking results. The Skill-automation approach also shows how prompt expertise is becoming a commodity that can be packaged and reused across agent tools.
Vague prompts like "a girl crying in the rain" force video-generation models to guess, yielding flat or oddly wrong footage. The fix is writing prompts as structured film-director instructions that pin down subject, scene, action, camera, style, lighting, emotion, and atmosphere in a single block. The article introduces SCELA+, a framework from ByteDance's Jimeng team, which orders prompts as Scene, Character, Expression/action, Lens language, and Atmosphere, plus a plus sign for lighting details, effects, and image references via @.
Beyond the framework, several techniques improve output fidelity: specifying camera movement by type and parameter (e.g., "push evenly in 3 seconds" beats "slowly push"); describing lighting across position, quality, color temperature, and shadow detail; translating abstract emotions into physical signals ("red eyes, trembling lips") since models don't grasp "sad" directly; avoiding negations because Seedance tends to generate what you write; and using numerical specs like time-segment axes or "motion intensity 7/10" for precise control. Prompt length should stay within 30–200 words; redundancy above 38% degrades action-following accuracy, and image references compress face descriptions while preserving consistency.
The author packages this knowledge as a reusable Skill called video-prompt-director, installable into agent tools like DeepSeek Harness, Claude Code, or Codex. The Skill walks the user through parameters to output a professional prompt, which then feeds into Doubao's Seedance or other video models. This bridges the gap between knowing the craft and executing it consistently.
The rule against negations reveals a concrete blind spot in current video models: they are conditioned on positive directives, so prompt engineering must work around it rather than assume improved instruction-following.
Packing the entire workflow into a Skill signals that prompt-writing is becoming a reusable technique rather than a one-off art, which suggests a growing market for prompt automation tools.
The emphasis on numeric parameters and time-axis segmentation implies that video models today need exact bounds to avoid drifting, a trait that will likely recede as models become better at handling context and ambiguity.
Doubao's Seedance and similar models may benefit more from disciplined prompt structure than from more sophisticated models, which puts the burden squarely on the user's clarity of intent.