seedance-20-prompt-optimizer

To optimize a rough prompt into a structured multimodal prompt, provide your initial text along with any image or video references. The system analyzes missing core elements like subject, action, and setting, then proposes a structured rewrite with explicit asset references.

The Seedance 2.0 framework for prompt engineering is a structure using three sections, eight core elements, and multimodal reference control. It transforms vague inputs into high-quality prompts with anti-ambiguity constraints and explicit asset anchors.

To convert a vague text prompt into a detailed video generation prompt, submit your rough idea and any multimodal references. The system identifies missing elements and applies clarifying questions to rewrite it into an unambiguous, structured output.

Yes, this prompt optimization workflow supports image and video references. It functions as a multimodal prompt director, anchoring your text with concrete references to ensure the final output has explicit asset controls.

Your AI prompt produces ambiguous multimodal results when it lacks core elements like subject, action, setting, and style. Applying a structured framework with anti-ambiguity constraints and explicit references resolves this issue.

The best way to add anti-ambiguity constraints to AI prompts is to follow a structured framework with eight core elements and multimodal reference control. This ensures your output contains explicit asset references and clear structural sections.