Beyond Using AI: Three Essential Skills for Everyone in the AI Era

1. Background

For decades humanity has upgraded tools—from abacuses to calculators, from paper documents to Excel, from manual search to search engines—always to help us do what we already could do faster. AI, represented by ChatGPT, breaks this boundary: it is no longer just a tool but a system that can participate in thinking, writing, coding, reasoning, and even suggesting decision paths. This changes the relationship from "people use tools" to "people collaborate with intelligent agents".

2. Mastering Prompt Engineering

Many assume using AI is as simple as opening ChatGPT and asking a question, but the quality of the result depends heavily on how the question is phrased. A basic request such as:

请帮我写一篇 AI 如何提升企业效率相关的方案

often yields a generic, unfocused answer that still requires manual refinement. An advanced prompt that defines role, task, constraints, and output format, for example:

你是一位资深咨询顾问 + 商业汇报专家，请帮我完成一份完整的PPT方案。

### 【任务】
制作一份主题为《AI如何提升企业效率》的汇报PPT，面向公司全体人员。

### 【要求】
1. 逻辑清晰，符合商业汇报结构
2. 内容有说服力，避免空话
3. 包含实际案例或可落地场景
4. 每一页内容简洁，适合PPT展示（不是长文章）

### 【输出步骤】
#### 第一步：输出完整PPT大纲
- 包含：首页标题、目录、各章节标题
- 整体结构要有逻辑递进（现状 → 问题 → 机会 → 方案 → 案例 → 总结）

#### 第二步：逐页展开内容
- 页面标题
- 3-5个核心要点（适合放在PPT上）
- 简短讲解说明（用于演讲时参考）

#### 第三步：优化与增强
- 标出哪些页面适合加入数据或图表
- 给出可视化建议（例如：对比图 / 流程图 / 漏斗图）
- 提供2-3个真实或通用案例（增强说服力）

### 【风格要求】
- 偏商业、理性、有结构
- 避免空洞表达，多用“结论 + 解释”方式
- 尽量让人一眼看懂重点

This contrast shows that a well‑crafted prompt can produce a high‑quality PPT in about 30 minutes, whereas a simple query may take 2–3 hours of manual work. The author explains that a good prompt is essentially a simplified requirement specification, consisting of four key elements:

Role : Define the persona the AI should adopt (e.g., product manager, investor, technical expert).

Task : State clearly what the AI must do (write, analyze, generate code, devise a plan, etc.).

Constraints : Specify limits such as word count, style, or structure.

Output Format : Indicate the desired format (Markdown, table, image, etc.).

Because AI only responds to the explicit input, vague prompts yield vague results; precise prompts yield higher‑quality outputs.

3. Building AI Capability

Prompt mastery is comparable to learning to walk; the next step is to move from using tools to building one’s own AI capabilities. This involves second‑stage development such as fine‑tuning open‑source models, deploying private instances, and creating controllable knowledge bases. The author identifies three long‑term concerns with relying solely on generic AI services:

Uncontrollable : Dependence on external platforms means capabilities can be restricted or withdrawn.

Lack of Business Depth : General models may not understand specific domain data or workflows without customization.

Data Leakage : Supplying proprietary data to a public service risks exposure and compliance issues.

Although building a proprietary AI system currently requires significant expertise, the author notes that open‑source models, toolchains, and deployment solutions are gradually lowering the barrier. Practical ways to start include:

Customizing open‑source models for specific tasks.

Constructing a personal knowledge base.

Running a controllable AI instance on local or intranet environments.

These steps create a safe, extensible AI system that belongs to the individual or organization.

4. Focusing on Real User Needs

The author likens AI to a rice cooker: electricity, compute, and algorithms form the hardware, while data is the rice that determines the final taste. High‑quality data is essential; without it, even the best hardware cannot produce good results. AI’s strength comes from massive training data, which also creates a natural boundary: it performs well on familiar patterns but can produce unstable or inaccurate answers on out‑of‑distribution queries.

As data becomes scarcer and more tightly regulated, relying solely on generic AI providers becomes less viable. Teams are expected to combine open‑source models with proprietary data to build tailored, controllable AI services, leading to a diversified ecosystem rather than a few dominant platforms.

5. Summary

Overall, the AI wave reshapes how we acquire and apply abilities. By mastering prompt engineering, gradually building personal AI development skills, and concentrating on genuine user problems, individuals can construct a resilient capability system. Rather than fearing replacement, the pragmatic approach is to use AI as an assistant, continuously expand one’s “calling‑ability” and maintain control over data and models.