Model-Agnostic Prompt Architecture Proposed for Stable AI Video Workflows

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A structured, model-agnostic approach to writing prompts for AI video generation has been outlined by a developer on DEV Community, aiming to solve the problem of prompts becoming obsolete after model updates. The method involves storing each shot as an intermediate structured representation — covering fields like subject, camera movement, action, and lighting — before translating it into any specific model's syntax. Key shot requirements are divided into invariants, which must survive every model translation, and preferences, which are negotiable stylistic details. A two-stage adapter then converts the structured data into model-specific prompts, separating visual state from motion instructions to simplify debugging. A lightweight validator is also recommended to catch errors such as unsupported durations, missing subjects, or timing beats that exceed the shot's total length.

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