Designing Model-Agnostic Prompts for AI Video Workflows

Short summary

The article proposes a model-agnostic prompt architecture for AI video generation: store creative intent as structured fields (subject, camera, action, lighting, etc.) rather than raw natural-language prompts, then use model adapters to translate that intermediate representation into each generator's preferred syntax. Fields are prioritized into invariants (P0: action order, camera direction, end state) and preferences (P1-P2), so when generation fails you preserve critical requirements first. Validation and structural evaluation replace pixel-similarity checks.

• •Store shot intent as structured JSON fields instead of model-specific prompts
• •Prioritize fields as invariants (P0) vs preferences (P1-P2) to preserve critical requirements during generation failures
• •Use model adapters to translate the stable representation into each generator's vocabulary, keeping source data model-agnostic

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