Understand Results读懂结果
Set expectations around time windows, metric availability, answer differences across AI apps or models, and how to save visual output.帮你建立对时间窗、指标可用性、不同 AI 应用或模型回答差异,以及如何保存可视化输出的预期。
Why different AI apps or models may differ?为什么不同 AI 应用或模型会给出不同结果?
If a request includes vague words like "best", "recent", or "high performing", different AI apps may choose different ranking metrics or time windows. Use explicit wording to make answers more stable.如果问题里有"最好""最近""表现好"这类模糊词,不同 AI 应用可能各自选不同的排序指标或时间窗。用明确的措辞能让回答更稳定。
What if a metric is not directly available?如果某个指标拿不到怎么办?
Ask the AI not to guess. Instead, ask it to use the closest proxy metric and say which assumption it made.让 AI 不要猜,而是用最接近的替代指标,并说明它做了什么假设。
FastMoss data types you can usually ask about你通常可以查询的 FastMoss 数据类型
Exact field availability can vary by tool and market. This map gives users a practical view of what types of data MCP can work with.具体字段是否可用因工具和市场而异。这张图让你直观了解 MCP 能处理哪些类型的数据。
Product Data商品数据
Shop Data店铺数据
Creator Data达人数据
Video / Content Data视频 / 内容数据
LIVE Data直播数据
Ads / Paid Signals广告 / 付费信号
Market / Category Trends市场 / 品类趋势
Agency / Partner Data机构 / 合作方数据
How to save visual answers如何保存可视化回答
If your AI app generates charts, calculators, or other rich visuals, the copy action may only capture the text portion. Save the visual separately when you need to share it. You can also ask the AI to include a plain table under every visual so the underlying numbers are easy to copy.如果你的 AI 应用生成了图表、计算器或其他富可视化内容,复制操作可能只会抓取文字部分。需要分享时请单独保存可视化内容。你也可以让 AI 在每个可视化下方附一张纯表格,方便复制底层数字。