用可迁移的工程方法，让一个人驾驭跨领域的多个AI工具。
Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains
- 将提示工程与上下文工程作为通用技能，跨领域组合使用多工具。
- 单人5个月完成10个工具串联，产出原需多人协作的结果。
- 提出六阶段流程和四项可衡量的技能迁移指标，适合技术管理者参考。
组织在专业领域中越来越多地部署专用AI工具，常需各领域专家配合，重演了AI本应革新的用工模式。然而，使这些工具高效的元技能——提示工程（交互优化）和上下文工程（结构化输入设计）——具有跨领域可迁移性：掌握者可在任意领域应用相同技能。本文定义「增强工程」为跨专业领域协同使用多个专用AI工具的系统性方法，以提示与上下文工程作为可迁移能力。我们提出六阶段编排方法论及四项可迁移性度量标准。一项为期5个月的形成性案例研究（2025年11月至2026年3月）记录了单一实践者在横跨七个专业领域的十组件工具链中应用该方法，生成了原本需多名专家协作的工作成果。两项定量观察符合框架预测：对两台聊天大模型共200次交互进行Cochran-Armitage趋势检验（p < 0.01），显示首次通过率随提示复杂度提升；对82个成果物进行Wright定律拟合（p < 0.01），表明生产效率持续加速。由于所有数据来自单一实践者，推断统计具探索性，旨在生成假设而非验证；全组合的可迁移性仍需多实践者复现。增强工程完成了三阶段演进：提示工程（单工具）、上下文工程（可复现管道）、增强工程（跨领域工具组合）。
原文摘要 · Abstract (English)
Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating the staffing models AI was expected to transform. Yet the meta-skills that make these tools effective, prompt engineering (interaction-level optimization) and context engineering (structured input pipeline design), are domain-portable: a practitioner who masters them can apply them to any purpose-built AI tool in any domain. This paper defines Augment Engineering as the discipline of orchestrating multiple purpose-built AI tools across distinct professional domains, applying prompt and context engineering as portable competencies that transfer across tool boundaries. We present a six-phase orchestration methodology and four portability metrics. A 5-month formative case study (November 2025 to March 2026) documents a single practitioner applying these skills across a ten-component orchestration stack spanning seven professional domains, producing work products that would traditionally involve separate domain specialists. Two quantitative observations are consistent with the framework's predictions: a Cochran-Armitage trend test (n = 200 interactions across two chat LLMs, p < 0.01) shows first-pass acceptance rising with prompt-sophistication level, and a Wright's Law fit (n = 82 artifacts, p < 0.01) shows production acceleration across the artifact portfolio. Because all observations come from a single practitioner, the inferential statistics are exploratory and hypothesis-generating rather than confirmatory; portability across the full portfolio awaits multi-practitioner replication. Augment Engineering completes a three-discipline progression: Prompt Engineering (one tool), Context Engineering (reproducible pipelines), Augment Engineering (a portfolio of tools across domains).
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