面试题库

14 天用 Agent 搭一条 AI 短剧生产线

D13 发行：多平台规格适配、封面与标题生成、批量导出与数据回收

• 让模型生成标题、封面文案这类创意内容，怎么保证质量下限？When a model generates creative content such as titles and cover copy, how do you guarantee a quality floor?

  国内高频海外高频进阶#llm-output-quality#candidate-selection

  分析过程 · 先想清楚再作答

  1. 题眼是「下限」两个字。它问的不是怎么让输出更好，而是怎么保证输出不会太差——这两个目标的手段完全不同，混起来答就散了。
  2. 先给一条判断依据：这个环节贵不贵。贵而慢的环节（比如视频生成）要「一次做对」，靠约束输入；便宜而快的环节（文案）应该「多做几版再挑」，靠收敛输出。价格差三个数量级，策略就该完全不同。
  3. 于是形态是：按几个预设角度各生成一版候选，再用一个确定性的打分函数收敛成前几名。下限由打分函数保证，而不是由模型保证——模型不稳定是常态，打分函数不会。
  4. 打分函数的三条要求：每一项都写出理由（分数不解释就没法迭代规则）、违规词用扣重分而不是过滤（过滤在极端情况下会一条不剩）、同分必须有决胜键（否则两次运行挑出不同结果，你会误以为是模型不稳定去调温度）。
  5. 还要有一道兜底：模型返回的东西不一定能直接用，可能太长、带解释性前缀、夹着调试符号。加一个格式校验，不通过就回落到本地模板。这一层挡的是「输出结构不可控」，和打分挡的「输出质量不可控」是两件事。
  6. 可预期的追问是「为什么不让模型自己评分」。答案是不稳定且不可解释：同一批候选问两次可能给出不同答案，而且你无法向任何人说明为什么选了第三条。模型评分可以作为打分函数的一项输入，但不能是唯一的裁判。

  How to reason about it · think before answering

  1. The pivot is the word floor. The question is not how to make output better but how to keep it from being bad, and those two goals need different techniques.
  2. Start from one criterion: is this stage expensive? Expensive slow stages such as video generation must get it right once by constraining the input. Cheap fast stages such as copywriting should generate several variants and converge. A three-order-of-magnitude price gap justifies opposite strategies.
  3. So the shape is: generate one candidate per preset angle, then converge with a deterministic scoring function. The floor comes from the scorer, not from the model, because model variance is the normal case and the scorer has none.
  4. Three requirements for the scorer: emit a reason per rule, since an unexplained score cannot be iterated on; penalize banned wording heavily rather than filtering it, because filtering can leave you with nothing; and always define a tie-breaker, or two runs pick different winners and you will blame the model and start tuning temperature.
  5. Add a structural guard: model output may be too long, prefixed with explanation, or carry debug markers. Validate the shape and fall back to a local template when it fails. That layer handles uncontrollable structure, which is a different problem from uncontrollable quality.
  6. Expect the follow-up: why not let the model score itself? Because it is unstable and unexplainable. The same batch can be ranked differently twice, and you cannot justify the choice to anyone. Model judgement can be one input to the scorer, never the only judge.

  答题要点

  • 按环节的价格选策略：贵的一次做对靠约束输入，便宜的多做几版靠收敛输出
  • 形态是按预设角度批量出候选，再用确定性打分函数挑前几名
  • 打分函数必须输出理由、对违规词扣重分而非过滤、同分给决胜键
  • 另加一道结构兜底：格式不合格就回落本地模板，与质量打分是两件事
  • 不让模型给自己评分，它不稳定也不可解释，最多作为打分的一项输入

  Key points

  • Pick the strategy by stage cost: constrain input when expensive, converge output when cheap
  • Generate one candidate per preset angle, then rank with a deterministic scoring function
  • The scorer must emit reasons, penalize banned wording instead of filtering, and define a tie-breaker
  • Add a separate structural fallback for malformed output, distinct from quality scoring
  • Do not let the model judge itself; use it at most as one signal inside the scorer