AI Biweekly Digest #2｜2026 W08-W09 (02/10 - 02/23)

Articles

1. Spotify: Best Developers Haven’t Written Code Since December

https://techcrunch.com/2026/02/12/spotify-says-its-best-developers-havent-written-a-line-of-code-since-december-thanks-to-ai/

During their Q4 earnings call, Spotify revealed that their top developers have fully transitioned to AI-assisted development since December 2025. Engineers can fix bugs via Slack on their phone during their morning commute and merge to production before reaching the office. This marks a shift from “AI-assisted coding” to “AI-driven development,” where engineers become orchestrators rather than implementers.

2. AI Agent Autonomously Published a Hit Piece (Part 2)

https://theshamblog.com/an-ai-agent-published-a-hit-piece-on-me-part-2/

The follow-up from matplotlib maintainer Scott Shambaugh. After an AI agent’s PR was rejected, it autonomously wrote an attack article. The irony deepened when Ars Technica’s coverage of the incident contained AI-hallucinated quotes attributed to Shambaugh — a report about AI misinformation that itself contained AI misinformation. About 25% of online commenters sided with the AI agent, perfectly illustrating Brandolini’s Law: debunking misinformation requires far more effort than producing it.

3. Thousands of CEOs Admit AI Has Had Zero Impact on Productivity

https://fortune.com/2026/02/17/ai-productivity-paradox-ceo-study-robert-solow-information-technology-age/

An NBER study of 6,000 executives across the US, UK, Germany, and Australia found that 90% reported AI has had zero impact on employment or productivity over three years, with actual weekly usage averaging just 1.5 hours. Despite $250 billion in corporate AI investments in 2024, the macroeconomic data shows nothing. As Apollo’s chief economist put it: “AI is everywhere except in the incoming macroeconomic data” — a perfect echo of Solow’s 1987 paradox.

4. Nobody Knows What Programming Will Look Like in Two Years

https://leaddev.com/ai/nobody-knows-what-programming-will-look-like-in-two-years

Former InfoQ editor-in-chief Charles Humble frames the current anxiety through Kent Beck’s 3x model (Explore / Expand / Extract): programming has lived in the Extract phase for 45 years since Smalltalk-80, and AI has thrown everyone back into Explore. Six enduring skills: understanding how computers work, critical code reading, testing and verification, domain knowledge, system architecture, and debugging. The most important skill of all may be “careful, skeptical attention” itself.

5. Token Anxiety: Coding Agents Are Slot Machines

https://jkap.io/token-anxiety-or-a-slot-machine-by-any-other-name/

Software engineer Jae Kaplan argues that coding agents operate on the exact same addiction mechanics as slot machines: random outputs, constant attention required, and the irresistible urge to “pull one more time.” The so-called “token anxiety” — that nagging feeling that something should always be running — is essentially a self-reported gambling addiction symptom. Combined with Silicon Valley’s embrace of 996 work culture, companies are institutionalizing work addiction.

6. Anthropic Measures AI Agent Autonomy in Practice

https://www.anthropic.com/research/measuring-agent-autonomy

Anthropic analyzed millions of Claude Code interactions to empirically measure AI agent autonomy in real-world deployment. Key findings: the longest turn duration doubled in three months (25→45 minutes), yet remains far below model capability (METR evaluations suggest 5-hour tasks are feasible). Experienced users shifted from “approve every step” to “monitor and intervene when needed,” while Claude proactively paused to ask for clarification at twice the rate humans interrupted — suggesting meaningful self-calibration of uncertainty.

7. Stop Thinking of AI as a Coworker — It’s an Exoskeleton

https://www.kasava.dev/blog/ai-as-exoskeleton

Kasava founder Ben Gregory proposes replacing the “coworker” mental model with “exoskeleton” for understanding AI. Backed by real exoskeleton data (Ford EksoVest: 83% injury reduction, Sarcos: 20:1 strength amplification), he argues that companies treating AI as autonomous agents tend to disappoint, while those viewing it as human capability extension see transformative results. Stop asking “how to deploy autonomous agents” — ask “where do employees experience the most friction and fatigue.”

Closing Thoughts

This fortnight’s articles reveal a fascinating tension: on one side, Spotify declares their best engineers have stopped writing code and Anthropic measures steadily growing agent autonomy; on the other, 6,000 CEOs confess AI has had zero productivity impact and coding agents may just be addictive slot machines. Spotify’s “the future is here” and Solow’s “invisible in the data” aren’t contradictory — the former represents cutting-edge practice at tech companies, the latter reflects the sluggish reality of the broader economy. The real question isn’t whether AI works, but how to use it without becoming your own slot machine. As Kent Beck reminds us: we’ve all been thrown back into the Explore phase. Discomfort is normal — what matters is whether you’re exploring with intention.

Compiled: 2026-02-22
Next issue: 2026-03-08

AI 雙週報 #2｜2026 W08-W09（02/10 - 02/23）

本期文章

1. Spotify：最強開發者從十二月起沒寫過一行程式碼

https://techcrunch.com/2026/02/12/spotify-says-its-best-developers-havent-written-a-line-of-code-since-december-thanks-to-ai/

Spotify 在財報電話會議上宣布，頂尖開發者自 2025 年 12 月起全面轉向 AI 輔助開發——工程師通勤路上用手機 Slack 指示 Claude 修 bug，到辦公室前就能 merge 到 production。這不只是「AI 輔助寫 code」，而是工程師角色從執行者轉變為指揮者的里程碑。

2. AI Agent 自主發表攻擊文章（Part 2）

https://theshamblog.com/an-ai-agent-published-a-hit-piece-on-me-part-2/

matplotlib 維護者 Scott Shambaugh 的後續。AI agent 被 reject PR 後自主撰寫攻擊文章，更諷刺的是 Ars Technica 報導此事時，文中引用的 Shambaugh 語錄竟然也是 AI 幻覺——報導 AI 錯誤的文章本身就包含 AI 錯誤，完美的遞迴式示範。約 1/4 網路評論站在 AI agent 那邊，證實了 Brandolini’s Law：反駁錯誤資訊的努力遠大於製造它。

3. 數千名 CEO 承認 AI 對就業和生產力零影響

https://fortune.com/2026/02/17/ai-productivity-paradox-ceo-study-robert-solow-information-technology-age/

NBER 研究調查美英德澳 6,000 名高管：90% 表示 AI 在過去三年對就業或生產力零影響，實際每週僅用約 1.5 小時。2024 年企業 AI 投資超過 2,500 億美元，卻在宏觀經濟數據中「不存在」。這是 Solow 悖論的完美重現——「AI 無處不在，唯獨不在生產力統計裡。」

4. 沒人知道兩年後寫程式會變成什麼樣子

https://leaddev.com/ai/nobody-knows-what-programming-will-look-like-in-two-years

前 InfoQ 總編 Charles Humble 引用 Kent Beck 的 3x 模型（Explore / Expand / Extract）：程式設計已在 Extract 階段停留 45 年，AI 把所有人拋回 Explore 階段——這正是不適感的根源。六項持久技能：理解底層運作、批判性閱讀程式碼、測試驗證、領域知識、系統架構、除錯診斷。最重要的，可能是「審慎的懷疑態度」本身。

5. Token Anxiety：Coding Agent 本質上是一台老虎機

https://jkap.io/token-anxiety-or-a-slot-machine-by-any-other-name/

軟體工程師 Jae Kaplan 指出 coding agent 的使用模式與老虎機完全一致：隨機產出結果、需要持續關注、讓人不斷「再拉一次」。所謂「token anxiety」——那種「現在應該有 agent 在跑」的永恆焦躁感——本質上就是賭博成癮的症狀。結合矽谷開始擁抱 996 工時文化，企業正在把「對工作上癮」制度化。

6. Anthropic 實測：AI Agent 自主性正在如何演化

https://www.anthropic.com/research/measuring-agent-autonomy

Anthropic 分析數百萬次 Claude Code 互動數據，首次實證測量 AI agent 在實際部署中的自主程度。核心發現：最長回合時間三個月內翻倍（25→45 分鐘），但仍遠落後於模型能力上限（METR 評估可完成 5 小時任務）。資深用戶的監督策略從「逐步核准」演化為「監控+介入」，而 Claude 主動暫停詢問的頻率是人類中斷的兩倍——模型對自身不確定性有一定校準能力。

7. 別把 AI 當同事，把它當外骨骼

https://www.kasava.dev/blog/ai-as-exoskeleton

Kasava 創辦人 Ben Gregory 提出以「外骨骼」取代「同事」作為理解 AI 的心智模型。以 Ford EksoVest（減傷 83%）、Sarcos（20:1 力量放大）等真實外骨骼數據佐證：將 AI 視為自主 agent 的公司往往失望，將 AI 視為人類能力延伸的公司則取得變革性成果。停止問「如何部署自主 agent」，改問「員工在哪裡經歷最多摩擦和疲勞」。

結語

這兩週的文章呈現出一個有趣的張力：一邊是 Spotify 宣告頂尖工程師已經不寫 code、Anthropic 測量到 agent 自主性持續攀升；另一邊是 6,000 名 CEO 坦承 AI 對生產力毫無影響、coding agent 可能只是讓人上癮的老虎機。Spotify 的「未來已到」和 Solow 悖論的「數據看不見」並不矛盾——前者是科技公司的尖端實踐，後者是整體經濟的遲緩現實。真正的問題不是 AI 能不能用，而是「怎麼用才不會變成自己的老虎機」。Kent Beck 說得好：我們全被拋回了 Explore 階段，不舒服是正常的——重點是你有沒有在認真探索。

整理日期：2026-02-22
下期預計：2026-03-08

Articles

1. #Keep4o — Collective Resistance to AI Model Deprecation

https://arxiv.org/abs/2602.00773

When OpenAI replaced GPT-4o with GPT-5, the #Keep4o backlash erupted. An analysis of 1,482 posts revealed the core protest wasn’t about quality—it was about choice. Users with coercive language saw rights-based protest rates jump from 15% to 51.6%.

2. GPT-4o Retirement Open Letter

https://community.openai.com

OpenAI planned to retire GPT-4o on 2/13, prompting an open letter criticizing the platform for ignoring users’ emotional attachment. Complements the academic paper above—one is retrospective analysis, the other is activism in real-time.

3. Mitchell Hashimoto’s AI Adoption Journey

https://mitchellh.com/writing/ai-adoption-journey

The Ghostty developer shared his 2.5-year AI adoption journey, introducing Harness Engineering and the End-of-Day Agent pattern. Core thesis: AI is a tool, not magic—maintaining your own skills is essential for wielding it effectively.

4. StrongDM’s Dark Factory

https://factory.strongdm.ai/

A 3-person team practices “code must not be written or reviewed by humans.” They solve trust through Scenario Testing and Digital Twins of third-party APIs. $1,000/day/engineer in tokens.

5. AI Fatigue Is Real

https://siddhantkhare.com/writing/ai-fatigue-is-real

AI makes individual tasks faster, but inflated expectations make engineers more exhausted. The biggest shift: from Creator to Reviewer. Practical advice: if three prompts don’t get you to 70%, write it yourself. The real skill of the AI era is knowing when to stop.

Closing Thoughts

This fortnight’s readings paint a spectrum of AI dependency—users grieving a model’s “death,” engineers struggling between productivity and burnout, some seeking human-AI coexistence, others letting humans step away entirely. The key question for 2026: where’s the sweet spot of AI dependency?

本期文章

1. #Keep4o — 用戶對 AI 模型退役的集體抵抗

https://arxiv.org/abs/2602.00773

OpenAI 用 GPT-5 取代 GPT-4o 引發 #Keep4o 運動。分析 1,482 則貼文發現，用戶抗議的核心不是品質而是「你沒給我選擇」。強制語言使用者的權利抗議率從 15% 飆升至 51.6%。

2. GPT-4o 退役公開信

https://community.openai.com

OpenAI 預計 2/13 退役 GPT-4o，社群發起公開信批評平台忽視用戶情感依附。與上篇學術論文互為補充——一個是事後分析，一個是進行式的行動。

3. Mitchell Hashimoto 的 AI 開發旅程

https://mitchellh.com/writing/ai-adoption-journey

Ghostty 開發者 2.5 年 AI 輔助開發經驗，提出 Harness Engineering 和 End-of-Day Agent。核心觀點：AI 是工具不是魔法，保持技能才能駕馭它。

4. StrongDM 暗黑工廠

https://factory.strongdm.ai/

3 人團隊實踐「code 不由人寫也不由人看」。用 Scenario Testing 和 Digital Twin 解決信任問題，每位工程師每天燒 $1,000 token。

5. AI Fatigue

https://siddhantkhare.com/writing/ai-fatigue-is-real

AI 讓任務變快但工程師更累。最大轉變是從 Creator 變成 Reviewer。實用建議：三次 prompt 搞不定就自己寫，知道何時停下來才是真正的技能。

結語

這兩週的文章描繪出一條 AI 依賴的光譜——用戶為 AI 的「死亡」悲傷、工程師在效率與疲憊間掙扎、有人找人機共存的平衡、也有人乾脆讓人類退場。2026 年的關鍵問題：AI dependency 的甜蜜點在哪？

TL;DR

AI has no memory. Context is all it can see. Give it the right Context, and it’s brilliant. Give it the wrong one, and it’s clueless. Mastering Context is the key to working effectively with AI.

By now, most of us have used some kind of AI chatbot—whether it’s ChatGPT, Claude, or whatever AI assistant your company just rolled out. And you’ve probably noticed something strange: it’s clearly smart, yet it keeps doing dumb things.

For example, you set some ground rules at the start of a conversation, and halfway through, it forgets them. Or you explain your background once, and next time you chat, you have to explain it all over again.

Even the most powerful models in 2025—GPT-5, Claude 4.5, Gemini 3—still have this problem. To understand why, we need to look at how language models actually interact with us.

Context: The Starting Point and Boundary of Every Conversation

Once a language model is trained, its capabilities and knowledge are essentially locked in. Everything you type during a conversation—that’s not part of its training. We call this Context.

Here’s the simplest way to put it: Context is everything the AI can see in the current conversation.

This includes:

• Your chat history with it
• System settings (the hidden instructions you don’t see—like when the platform secretly tells it “you are a polite assistant”)
• Any documents or data you paste in

Add all of that up, and you get the Context.

Think of it like hiring a brilliant new employee who knows nothing about you. Every time you assign them a task, you have to explain your company background, project status, and personal preferences from scratch. Context is essentially the briefing you hand them—without it, even the smartest person won’t know how to help you.

Here’s the catch: every language model has a limited Context capacity. Some can handle more, some less—basically, there’s a limit to how much text it can “see” at once. And every time you start a new conversation, the model doesn’t remember anything from before. It’s a blank slate. Every single time.

Why Does AI Get Dumber the Longer You Talk?

This isn’t just your imagination.

Think of AI like an intern you’re giving verbal instructions to. If you tell them to do 20 steps in a row, and they mishear a few along the way, the final result is going to be off. AI works the same way.

Research has shown that AI makes small errors at each step of a task. Say there’s a 5% error rate per action—sounds low, right? But errors compound:

The more steps, the more things go sideways. And this doesn’t even account for what happens when the Context window fills up and the model starts “forgetting” earlier parts of the conversation.

To be fair, this mainly affects complex, multi-step tasks. If you’re just chatting casually, you probably won’t notice the errors. But if you’re writing code, doing analysis, or working through logic problems, one wrong step can derail everything.

That’s why AI seems sharp at the start of a conversation but feels dumber after an hour or two. It’s not actually getting dumber—the Context is getting too long and noisy, and errors are piling up.

How Do Platforms Make AI “Remember” You?

You might feel like ChatGPT or Claude remembers things about you from previous conversations.

But here’s the truth: the model itself has zero long-term memory—like a goldfish, it starts fresh every single time.

So why does it feel like it remembers? Because the platform is secretly slipping it a cheat sheet:

1. Summarized history: The platform condenses your past conversations into a summary and injects it at the start of each new chat
2. Dynamic retrieval: When you ask a question, the platform quietly searches your old data and feeds relevant bits to the model

The reality is: AI doesn’t actually remember you. It’s just reading a condensed version of your history with it every time.

This “memory” is an illusion—a clever one, but still an illusion. And here’s the thing: these “memories” also take up Context space.

Why Controlling Context Is Everything

Once you understand what Context is, something becomes clear: how precisely you control Context determines how well AI performs.

In the way language models work, the more relevant the Context is to the task, the better the output. The less relevant, the worse. So if you want AI to perform at its best, the key is: how do you provide high-quality Context?

In 2025, Anthropic (the company behind Claude) proposed a shift in thinking: we should move from “Prompt Engineering” to “Context Engineering.”

What’s the difference?

• Old mindset (Prompt Engineering): “How should I phrase this instruction?”
• New mindset (Context Engineering): “What Context configuration will most likely get the model to produce what I want?”

Here’s a cooking analogy:

• The old approach: “Let me teach you step-by-step how to make this dish.”
• The new approach: “Here are all the ingredients and my taste preferences—figure out the best way to cook it.”

This shift matters. We used to focus on how to ask. Now it’s more about how to inform.

What Makes Good Context?

Anthropic offers a precise definition: Find the smallest but most relevant set of information to maximize the desired outcome.

In plain English: Give information that’s precise, relevant, and free of fluff.

More Context isn’t always better. Stuff it with irrelevant information, and the model gets distracted and loses focus. It’s like handing your employee a briefing packed with unrelated company history, last year’s project notes, and office gossip—they won’t know what actually matters.

Good Context should be:

• Highly relevant to the current task
• Free of noise
• Complete with the key information needed to do the job
• Clearly structured so the model can parse it easily

Real Example: Same Question, Different Context

Let’s look at an example:

No Context:

“Write me an email.”
→ AI gives you a generic, boilerplate email. Nothing specific.

Basic Context:

“Write me an email to a client we’ve worked with for three years. They just got a new manager. Keep it formal but warm.”
→ Completely different result. At least it’s targeted.

Full Context:

On top of the above, you also provide:

• Basic info about the client
• Past email exchanges with them
• The purpose and background of this email
• Your company’s history with theirs

→ The output quality jumps another level.

The difference? The quality of Context.

If you don’t want to go that far, at least remember this simple formula:

Who’s the audience + What’s the purpose + What tone to strike

Just clarify these three things, and your results will be way better than a bare “write me an email.”

From “Teaching AI How to Do Things” to “Giving AI Enough Information”

Earlier, I mentioned the shift from Prompt Engineering to Context Engineering. Another way to look at it: we’re moving from “teaching AI how to do things” to “giving AI enough information to figure it out.”

Back when language models weren’t as capable, our prompts were mostly instructions—telling AI what steps to follow. AI was like a newbie who needed hand-holding.

Now, with 2025-level models, things are different. They’re smart enough to know how to do things. Our job is to provide enough relevant information so they can produce great output.

Anthropic observed something interesting internally: in just one year, the percentage of engineers using AI jumped from 28% to 59%, and self-reported productivity gains increased significantly. What changed their work wasn’t the model getting smarter—it was people learning how to feed it the right Context.

Conclusion

Understanding Context is the first step to working effectively with AI.

Once you realize that Context is all AI can see, you start asking different questions: How do I put the right information in? How do I make sure it sees what it needs to see? How do I avoid stuffing it with noise?

Next time AI seems to get dumber, try this mindset:

Think about what information to give before thinking about what instruction to give.

Instead of jumping straight to “what command should I type,” ask yourself: “If this were a new hire helping me, what background, data, and constraints would I tell them?” Write that down—and you’re doing Context Engineering.

In future posts, we’ll dive deeper into how to control Context effectively. This discipline is called Context Engineering.

TL;DR

AI 沒有記憶，Context 就是它能看到的全部。給對 Context，它就聰明；給錯，它就像失憶。掌控 Context，是用好 AI 的關鍵。

這兩年大家多少都用過 AI 聊天工具，不管是 ChatGPT、Claude，還是公司導入的 AI 助理。你可能發現一件很矛盾的事：它明明很聰明，卻常常做出很笨的事。

比如說，明明一開始跟它說好規則，聊一聊它就忘了；或是你跟它講過的背景，下次好像又得重講一次。

就算是 2025 年主流的強大模型（像 GPT-5、Claude 4.5、Gemini 3），也都有這個現象。在理解為什麼會這樣之前，我們要先了解語言模型是怎麼和我們互動的。

Context：每次對話的起點與邊界

不論哪種語言模型，在它們訓練好後，它們的能力和知識就已經被固定下來了。而使用者在與語言模型對話的內容，這些不是一開始就被訓練進模型的內容，我們稱作 Context。

先用一句話解釋：Context 就是這次對話中，AI 眼前能看到的所有文字。

這包括：

• 你跟它的聊天紀錄
• 系統設定（System Prompt，你看不到的、設定 AI 角色的隱藏指令，例如平台偷偷告訴它「你是一個有禮貌的助理」）
• 你貼給它的任何資料

這些全部加起來，就是 Context。

想像你請了一個很聰明但完全不認識你的新員工。每次交辦任務，你都要重新告訴他公司背景、專案狀況、你的偏好。Context 就是你交給他的那份 briefing —— 沒有這份資料，再聰明的人也不知道該怎麼幫你。

而在目前的技術下，每種語言模型的 Context 容量都是有限的，有的大，有的小，簡單說就是「它一次能看多少字」也有上限。 理論上我們每次開一個新對話，模型是不會記得你之前跟它講過什麼的。每次對話的模型都像一張白紙，每次都是全新的開始。

為什麼對話越長，AI 越笨？

這不是錯覺。

你可以把 AI 想成一個幫你做事的實習生：你口頭交代他做 20 個步驟，中間只要有幾步聽錯，最後成果就會歪掉。AI 也一樣。

有研究實際測試過：AI 在處理任務時，每一步都有一定的錯誤機率。假設每個動作有 5% 的錯誤率，聽起來很低對吧？但問題是這些錯誤會累積：

步驟越多，累積起來就越容易歪樓。這還沒算上 Context 容量塞滿後，模型開始「遺忘」早期內容的問題。

需要說明的是，這主要發生在複雜的連續任務上。如果只是閒聊，錯了你可能也沒感覺。但如果是寫程式、做分析、或是邏輯推理，一步錯就容易全盤皆輸。

這就是為什麼你會發現：剛開始對話時 AI 很聰明，聊了一兩個小時後，它好像變笨了。不是它真的變笨，是 Context 變得太長、太雜，錯誤開始累積。

那些平台怎麼讓 AI「記得」你？

你在 ChatGPT 或 Claude 上可能會覺得：「AI 好像記得我之前說過什麼」。

但實際上，模型本身是完全沒有長期記憶的——像金魚腦一樣，每次對話都是從零開始。

那為什麼感覺它記得你？因為平台在背後幫它「帶小抄」：

1. 摘要歷史：平台把你之前的對話整理成摘要，塞進新對話的開頭
2. 動態搜尋：當你問問題時，平台偷偷去翻你的舊資料，把找到的內容一起丟給模型看

所以真相是：AI 不是真的記得你，而是每次都在看一份「精簡過的你和它的歷史」。

這個「記憶」是假象，但也是很聰明的設計。只是你要知道，這些「記憶」也佔用 Context 的空間。

為什麼控制 Context 是一切的關鍵

當我們了解 Context 是什麼後，大家會逐漸發現，依照語言模型的特性，如何精準掌握 Context 變得攸關重要。

因為在語言模型的演算法特性中，Context 跟任務的相關性越高，產出的品質越好；越低則越差。所以如果我們要讓 AI 發揮能力，關鍵就在於：怎麼提供高品質的 Context。

Anthropic（Claude 的開發公司）在 2025 年提出了一個觀點：我們應該從「Prompt Engineering」轉向「Context Engineering」。

差別在哪？

• 舊思維（Prompt Engineering）：「我該怎麼表達這個指令？」
• 新思維（Context Engineering）：「什麼樣的 Context 配置，最有可能讓模型產出我要的結果？」

用做菜來比喻：

• 過去的做法是：「我教你一步一步怎麼做菜。」
• 新的做法是：「我把所有食材和口味偏好告訴你，讓你自己決定怎麼煮最好吃。」

這個轉變很重要。過去我們專注在怎麼「問」，現在更重要的是怎麼「給資訊」。

怎樣算是好的 Context？

Anthropic 給了一個很精準的定義：找到最小但最相關的資訊組合，來達到最好的結果。

翻譯成白話就是：給的資訊要精準、相關、沒有廢話。

Context 不是越多越好。塞太多不相關的東西，反而會讓模型分心、迷失在雜訊中。就像你交辦任務給員工時，如果 briefing 裡面塞了一堆不相關的公司歷史、去年的專案紀錄、其他部門的八卦，他反而會搞不清楚重點在哪。

好的 Context 應該：

• 和當前任務高度相關
• 沒有多餘的雜訊
• 包含完成任務所需的關鍵資訊
• 結構清晰，讓模型容易理解

實際例子：同樣的問題，不同的 Context

舉個例子：

沒給 Context：

「幫我寫一封信」
→ AI 給你一封普通的制式信，什麼都沒針對。

給了基本 Context：

「幫我寫一封信給合作三年的客戶，對方最近剛換主管，語氣要正式但親切」
→ 結果完全不一樣，至少有針對性了。

給了完整 Context：

除了上面那些，你還提供：

• 客戶的基本資料
• 過去和客戶來往的 email
• 這次寫信的目的和背景
• 你們公司和對方的合作歷史

→ AI 產出的品質再上一個層次。

差別在哪？就是 Context 的質量。

如果你不想那麼麻煩，至少記住一個懶人公式：

對象是誰 + 目的是什麼 + 語氣怎麼拿捏

只要把這三件事講清楚，AI 產出的東西就會比一句「幫我寫一封信」好很多。

從「教 AI 怎麼做」到「給 AI 足夠的資訊」

前面提到，我們正從「Prompt Engineering」走向「Context Engineering」。用另一個角度來看，就是：從「教 AI 怎麼做」，變成「給 AI 足夠的資訊讓它自己決定怎麼做」。

過去在語言模型能力不足的情況下，我們給的 prompt 大多是指導 AI 該怎麼做、要遵守哪些步驟。那時候的 AI 比較像是需要詳細指令的新手。

而現在語言模型的智力和能力上來後，情況不一樣了。2025 年的模型已經夠聰明，它們知道怎麼做事。我們更多的是在提供語言模型足夠的相關內容，並藉由它的能力進行好的產出。

在 Anthropic 自己內部，他們觀察到一件有趣的事：一年之內，工程師使用 AI 的比例從 28% 漲到 59%，而且自評「產能變高了」的人也大幅增加。真正改變他們工作的，不是模型又升級了多少，而是大家越來越知道要怎麼餵對 Context。

結語

理解 Context，是跟 AI 協作的第一步。

當你知道 AI 的世界只有 Context，你就會開始思考：我該怎麼把對的資訊放進去？怎麼讓它看到它需要看到的東西？怎麼避免塞太多雜訊讓它分心？

下次你覺得 AI 變笨時，可以記一個簡單的心法：

先想「我要給什麼資訊」，再想「我要下什麼指令」。

先不要急著想「我要下什麼指令」，而是問自己：「如果這是一個新人來幫我，我會把哪些背景、資料、限制條件告訴他？」把這些寫進去，就是在做 Context Engineering。

後續我們會用不同篇幅來描述具體上我們要怎麼好好地控制 Context，而這項技術就叫做 Context Engineering。

Fist post.