AI 编导AI Scriptwriting
介绍Introduction
大量训练蒸馏出来的编导模型，直接出爆款文案。A scripting model built through extensive training and distillation, producing high-performing copy directly.
编导是整条内容链路的上游。文案不行，后面拍得再好、发得再多都是白费。Scripting sits upstream of the entire content chain. If the copy is weak, no amount of shooting quality or posting volume rescues it.
我们做了大量的训练和蒸馏，模型出的稿子直接能用，不是给你一堆需要重写的草稿。We have put substantial training and distillation behind this, so the model's output is usable as-is rather than a pile of drafts needing rewrites.
我们不是拿通用大模型直接写。是把已经跑出成绩的爆款稿子喂进去做训练和蒸馏，模型学到的是这个品类里真的能起量的写法。This is not a general-purpose model writing straight off the prompt. Proven hits from the category go in as training and distillation material, so what the model learns is the way copy actually performs in that category.
出来的是成稿加分镜，不是选题建议。编导拿着就能进下一环，不用先花半天把 AI 的草稿改成人话。What comes out is a finished script plus a shot list, not a list of suggestions. A director can take it straight to the next stage instead of spending half a day turning an AI draft into something a person would say.
选题库按门店或品类定制，所以同一套系统给口腔和给餐饮出的稿子不是一个味道。The topic library is tailored per store or category, so the same system writes in a noticeably different register for a dental clinic than for a restaurant.
- 成稿文案与分镜表Finished scripts and shot lists
- 按门店或品类定制的选题库Topic libraries tailored to store or category
- 品类爆款结构标注与训练集Structural annotation of category hits, and the training set built from it
- 批量出稿（同选题多版本）Batch output: many versions of the same topic
- 数据回流与选题库迭代Performance data fed back, and the topic library iterated on it
- 编导交接文档（拍摄可直接执行）A handover document a shoot can be run from directly
怎么做How it runs
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喂爆款做训练Train on the hits
收集这个品类已经验证过的爆款文案，标注它们起量的结构特征，做训练和蒸馏。模型学的是这个品类的语感，不是通用文风。Collect the copy that has already proven itself in the category, annotate the structural features behind its performance, then train and distil. What the model picks up is the category's own register, not a generic voice.
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定选题库Build the topic library
按门店或品类把选题拆成可复用的库：痛点类、对比类、科普类、活动类，每类给到具体角度而不是抽象方向。Break topics into a reusable library per store or category, with concrete angles rather than abstract directions.
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出成稿与分镜Write to shot level
一次出到分镜层：画面、台词、时长、镜头数全给全，编导不用二次拆解。Go all the way to shot level in one pass, with framing, lines, durations and shot count all specified, so nothing has to be broken down a second time.
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回流复盘Feed the results back
发出去的数据回流进选题库，起量的写法加权、扑街的下架，模型和选题库一起迭代。Performance from what actually got posted flows back into the library: what performed gets weighted up, what failed comes out, and model and library iterate together.