Mechanisms for guaranteeing narrative coherence
To ensure that the visual output faithfully reflects the intent of the text, the following quality control methods are recommended:
- Three-level Prompt Design Method::
- Macro level: define the tone of the story in the -subject (e.g. "suspenseful", "romantic").
- Mesoscopic layer: ref_panel_prompt identifies core visual metaphors
- Micro level: panel_prompts use a standardized structure of action verbs + environmental adjectives
- Semantic anchor checking: Compare keyword coverage (frequency of occurrence of important nouns/verbs) using detailed prompt records in the generation logs
- Iterative generation strategy: Adopt a "generate-evaluate-correct" cycle, test with a short version of the prompt first, then add details step by step
Risk avoidance: avoid mixing more than 3 core elements in a single prompt, for key plot turning points, manual splicing after separate generation is recommended. A visual lexicon can be created to save prompt combinations of successful cases.
This answer comes from the articleStory2Board: generating coherent split-screen scripts from natural language storiesThe
May not be reproduced without permission:AI productivity tools " How do you prevent the generated sub-scripts from deviating from the narrative intent of the original text?
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