用 Fable 5.2 四个提示词完成过去需数天的 STATA 基准分析
Ethan Mollick 亲自对比了 STATA 时代和现在的分析效率：以前数天的基准分析加可视化，现在 Fable 5.2 四个提示词搞定。做研究或数据分析的人可以看看他怎么拆解提示词，想想自己的统计流程哪些环节能交给模型跑。
Ethan Mollick 在 X 上分享了一个对比：几年前完成同类基准分析并用 STATA 做出可视化效果，需要数天工作量，排版美化也要花不少时间。如今他只用了 4 个提示词调用 Fable 5.2，就生成了完整的基准分析和成品图表。这条推文本身是一个工作量对照：STATA 流程约数天，Fable 5.2 流程约 4 次提示交互。
从工作流看，他的做法是把统计分析和结果呈现整体交给模型处理，不再分步在 STATA 中写代码、调参、导出图表再排版。这意味着过去依赖统计软件手动操作的分析链条，可以压缩成几次对话。
对研究者而言，这类对照的实际意义在于时间成本结构的变化：数据分析门槛从“会用 STATA”转向“会描述分析需求”。具体节省量取决于任务复杂度，但推文给出的对照是明确的：数天 vs 4 个提示词。
As a side note, a couple years ago, making this benchmark & analysis would’ve taken a considerable amount of time in STATA, likely several days, and making it look this nice would have been quite a while as well.
This was like 4 prompts to Fable 5.2
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Fable 5.2, STATA, data analytics, prompt engineering.
Source attribution: curated from Ethan Mollick (@emollick). Cross-source corroboration: 10 independent sources have covered this development (including 向阳乔木, fal, Ate-a-Pi), confirming industry-wide signal strength.
