# How we prompt AI is very different in 2026 than 2022 when ChatGPT came out.

I'm teaching a new cour...

Canonical URL: https://www.traeai.com/articles/125135d7-a265-4a7b-ad9d-079445e8331c
Original source: https://x.com/AndrewYNg/status/2049886895530967534
Source name: Andrew Ng(@AndrewYNg)
Content type: tweet
Language: 中文
Score: 5.2
Reading time: 2 分钟
Published: 2026-04-30T16:21:35+00:00
Tags: AI, Prompt Engineering, Education, LLM

## Summary

该帖为Andrew Ng推广其新课《AI Prompting for Everyone》的宣传文案，提及2026年提示工程已显著进化，但未提供具体技术细节或实证分析。

## Key Takeaways

- 提示工程实践在2026年已较2022年有实质性演进，强调多模态输入与深度推理模式。
- 课程主张跨模型通用提示技能，覆盖ChatGPT、Gemini、Claude等主流工具。
- 强调上下文增强（文档/图像）、AI长时思考决策、多任务生成（图像/数据/网站）等高阶用法。

## Outline

- 引言：提示范式已变迁 — 指出2026年AI提示方式与2022年ChatGPT初代相比发生根本性变化。
  - 课程定位与受众 — 面向所有技能水平学习者，目标是培养跨平台AI高效使用者。
  - 核心能力模块 — 涵盖深度研究模式、多模态上下文注入、AI长时思考、多模态生成等实用技能。
    - 底层认知补充 — 加入模型工作原理直觉，辅助用户判断答案可信度。

## Highlights

- > How we prompt AI is very different in 2026 than 2022 when ChatGPT came out. — 原文首句
- > How to give AI the right context, including more documents and images than most people realize you can provide. — 原文中段
- > When to ask AI to think hard for several minutes on important decisions like what car to buy, what to study, or what job to take. — 原文中段
- > I also cover intuitions about how these models work under the hood, so you know when to trust an answer and when not to. — 原文末段

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