AI Workflow & Enablement Portfolio

01 | 项目概述 Project Summary

一个轻量级 AI 工作流原型,用来把混乱的项目输入(甲方反馈、会议纪要、碎片化需求)转化为结构化、可追踪、可复用的输出——服务于项目推进与汇报沟通。

A lightweight AI workflow prototype that converts messy project inputs — client feedback, meeting notes, fragmented requests — into structured, traceable outputs for project execution and leadership communication.

这不是几个互不相关的小助手拼在一起,而是一个以 workflow 为核心的项目系统:一个主原型 + 多个基于不同输入场景的验证分支。整套作品集要证明的不是"会用 AI",而是设计结构化 workflow、处理不确定性、并把方法沉淀成可教学可复用的东西。

This is not a collection of unrelated AI assistants — it is a single workflow-oriented project system: one main prototype plus scenario-based validations. What it aims to prove is not "AI usage," but the ability to design structured workflows, surface uncertainty explicitly, and turn methods into something teachable and reusable.

02 | 为什么做这个 Why I Built This

在设计与项目场景里,关键信息经常分散在聊天、反馈和零碎记录中。这会带来五个反复出现的问题:

In design and project environments, important information is often scattered across conversations, feedback, and informal notes — creating five recurring problems:

• 任务遗漏 —— Key tasks easily missed
• 风险暴露滞后 —— Risks surface too late
• 未知信息被掩盖 —— Unknowns hidden instead of made explicit
• 输出不一致难复用 —— Outputs inconsistent and hard to reuse
• 汇报内容反复重写 —— Leadership communication rebuilt from scratch every time

这些不是"使用 AI 工具就能解决"的问题,而是需要设计一套结构化的工作流,才能让 AI 的输出真正落到执行层和汇报层。

These are not problems that "using an AI tool" can fix. They require designing a structured workflow to make AI output actually land at the execution and communication layers.

03 | 工作流怎么设计 Workflow Design

整套 workflow 围绕一个固定输出 schema展开,每个模块承担明确的功能:

The entire workflow is built around a fixed output schema, with each section serving a specific purpose:

输出结构 A–E / Output Structure A–E

• A. Executive Summary —— 30 秒可读完的汇报摘要,服务领导决策
• B. Task Table —— 可追踪任务表,含 Owner / Due / Priority / Dependency / Source Quote
• C. Risks & Mitigations —— 前置暴露的风险与对策
• D. Open Questions —— 显性化的未确认问题
• E. Assumptions & Source —— 假设与原句引用,用于追溯与边界控制

每个模块的设计目的:Executive Summary 让领导快速读懂、Task Table 落到执行、Risks 提前暴露、Open Questions 显性管理未知、Assumptions & Source 通过原句引用降低幻觉风险。

Each module serves a specific role: Executive Summary for fast leadership readability, Task Table for execution tracking, Risks for early surfacing, Open Questions for explicit unknown management, Assumptions & Source for traceability and hallucination reduction.

治理原则 / Governance Principles

为了让 AI 输出可靠、可追溯、可审阅,这套 workflow 定了几条硬规则:

To make AI output reliable, traceable, and reviewable, the workflow enforces several hard rules:

• 不编造缺失信息 —— No fabrication of missing facts, dates, owners, or decisions
• 未知信息显性化 —— Unknowns go into Open Questions / Assumptions, never hidden in Notes
• 原句溯源 —— Important interpretations link back to source quotes
• 字段规范 —— Due field is date-only or blank; Priority uses only P0–P3; no relative time
• 已知与未知分离 —— Known execution data and unresolved questions stay separate
• 对外使用前必须人工审核 —— Human review required before any external-facing communication

字段规范细则 / Field Discipline

• Priority —— 仅使用 P0 / P1 / P2 / P3
• Due —— 仅使用具体日期或留空,不使用"下周""明天"等相对时间
• Owner —— 负责人或 TBD,不留空
• Notes —— 仅放执行备注,不作为未知信息的兜底
• 任务用动作动词 —— "Define three direction strategies"(优), "Think about options"(劣)
• 输出语言明确控制 —— 标题双语 / 主体中文 / 数据库字段名英文

04 | 场景验证 Scenarios

这套 workflow 在 4 个真实场景下做了验证。每个 case 都包含真实的 messy input + 完整的 A-E 结构化输出,用来证明同一套 workflow 在不同类型的混乱输入下都能成立。

The workflow was validated across 4 real scenarios. Each case captures real messy input + the full A-E structured output, demonstrating that the same workflow holds across different types of messy input.

4 个 case 共享的底层逻辑 / Unified Logic

虽然 4 个场景输入类型完全不同——客户变更 / 领导请求 / 内部会议 / 需求文档——但底层逻辑是一致的:

Although the 4 scenarios differ entirely in input type — client change / leadership request / internal meeting / requirement document — they share the same underlying logic:

• 把混乱输入转成结构化输出 —— Turn messy input into structured output
• 区分已知与未知 —— Separate knowns from unknowns
• 前置暴露风险 —— Surface risks early
• 支持后续行动 —— Support downstream action
• 提升可追踪性和复用性 —— Improve traceability and reuse

输入类型决定 schema 走向——对话型输入更易导向 actions,文档型输入更易导向 checklists 和 gaps。这就是为什么 schema 在不同场景下有差异化扩展,而不是强行用一张表打天下。

Input type drives schema direction — conversation inputs lean toward actions, document inputs lean toward checklists and gaps. That's why the schema has scenario-specific extensions rather than forcing one rigid table across all cases.

05 | 治理与边界 Governance & Boundary

这套 workflow 能做什么、不能做什么,作为创作者需要写清楚。

What this workflow can and cannot do — written plainly by its creator.

它不是什么 / What It Is Not

• 不是决策替代品 —— 它帮助结构化思考、暴露任务和风险,但不替代设计判断、领导拍板、甲方确认
• 不是工程验证器 —— 可用于概念阶段规划与沟通,不能用来证明技术可行性、规范符合性、可施工性、造价确定性
• 不是 prompt 一次性输出 —— 是一套带有结构、治理、迭代、追溯的系统,不是"扔个 prompt 给 AI"

This workflow is not a decision-maker, not an engineering validator, and not a one-shot prompt. It is a system with structure, governance, iteration, and traceability.

5 种常见失败模式 / 5 Common Failure Modes

每一种失败都对应明确的修正方式——这些修正不是"再试试 prompt",而是在 prompt 和 review 中把规则写死:

Each failure mode has a defined fix — and the fix is not "try a different prompt," but encoding the rule into the prompt and review process:

• 语言混用 —— Mixed languages → 显式定义输出语言规则
• 字段漂移 —— Schema drift(优先级格式 / 相对时间)→ 锁定允许的 schema 值
• 任务太空 —— Tasks too abstract(主题而非行动)→ 要求 action-oriented 措辞
• 未知信息被编造 —— Unknowns fabricated(日期 / 责任人 / 决策)→ 要求 missing info 进 Open Questions / Assumptions
• 原句引用过度改写 —— Quotes too loose(被改写过度)→ 要求保留原始措辞

敏感数据最小化 / Sensitive Data Minimization

在使用 AI 处理项目资料前,敏感或保密信息应尽量减少或脱敏:身份信息、保密客户资料、内部预算细节、未公开决策内容。

Before using AI on project materials, sensitive or confidential information should be minimized or redacted: personal identifiers, confidential client information, internal budget details, non-public project decisions.

06 | 可教学可复用 Enablement

这套 workflow 的设计目标不只是让一个人用得起来,还要能教、能复用、能在相似场景中扩展。

The workflow is designed not only to work for one user, but to be teachable, reusable, and scalable across similar project scenarios.

谁适合用 / Who This Is For

经常处理碎片化项目信息的人:项目协调 / PMO / 运营、负责汇报的设计人员、处理甲方变更的团队成员、需要把混乱输入转成结构化行动的人。

Project coordinators, PMO / operations roles, designers preparing leadership updates, team members handling client changes — anyone translating messy inputs into structured actions.

5 步上手 / Quick Start

1. Prepare raw input —— 选一个 messy 输入源(甲方反馈 / 会议纪要 / 领导请求 / 内部聊天)
2. Use the standard prompt —— 把输入粘贴进固定 prompt 模板
3. Generate structured output —— 让 AI 输出 A-E 五大块
4. Review before reuse —— 检查信息完整性、结构合规性、原句引用、任务可执行性、语气适配性
5. Add tasks into shared database —— 把相关任务移入 Notion 任务数据库,含 Priority / Owner / Due / Dependency / Status / Project / Source Quote

一句话教学脚本 / One-line Teaching Script

我们不是让 AI 替代判断,而是让它先把混乱输入变成一个结构化初稿,帮助我们更快暴露任务、风险和缺失信息;真正用之前仍然要人工审核。

We use AI here not to replace judgment, but to turn messy inputs into a structured first draft. The workflow helps surface tasks, risks, and missing information faster. We still review everything before real communication.

当前已具备 / Currently in Place

固定输出 schema、可复用 prompt 结构、Notion 任务数据库、4 个场景验证案例、多轮 prompt 迭代记录。

Fixed output schema, reusable prompt structure, Notion task database, 4 scenario validations, multi-round prompt iteration records.

07 | 反思与下一步 Reflection & Next

当前状态 / Current Status

原型完成 + 初步验证。已完成结构设计、模板设计、Notion 数据库、4 个场景案例、两轮 prompt 迭代验证。第一轮暴露语言混用与字段漂移,第二轮通过加强 prompt 约束后,输出在语言一致性、结构稳定性、字段规范性上都有显著提升。

Prototype complete + early validation. Structure design, template definition, Notion database, 4 scenario cases, and two rounds of prompt iteration are all done. Round 1 exposed language inconsistency and schema drift; Round 2 significantly improved language consistency, schema compliance, and field discipline after tightening prompt rules.

已知 gap / Known Gaps

• 任务粒度仍可更 action-oriented
• 负责人分配规则仍偏保守(更多 TBD 而非具体角色)
• AI 输出与 Notion 数据库字段的对齐还不够完整
• 场景测试覆盖可继续扩展(更多输入类型、更多领域)

下一迭代方向 / Next Iteration

• 更清晰地定义"核心 schema + 扩展 schema"模型,让不同场景的字段差异有更明确的标准
• 进一步对齐 Notion 数据库,实现 AI 输出直接落库
• 加强任务与原句映射,提升 traceability
• 建立更明确的 review checklist,降低复用前的人工检查成本
• 视情况做多模型对比测试

一句话定位 / One-line Positioning

这是一套 workflow 系统在多个场景下的验证,不是多个互不相关的小工具。我擅长把混乱的项目输入,转化为结构化、可追踪、可复用的 AI 工作流输出。

This is one workflow system validated across multiple scenarios, not several unrelated tools. I design AI-assisted project workflows that turn messy inputs into structured, traceable, and reusable outputs.