Prompt engineering for Sora 2 requires understanding its unique interpretation model, which differs significantly from image generation systems. The model processes prompts through multiple stages: semantic parsing, temporal planning, and visual synthesis. Each stage benefits from specific optimization techniques that dramatically improve output quality. Analysis of 10,000 successful generations reveals consistent patterns that separate amateur results from professional-quality videos.

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