Learning to prompt won’t help you master AI. The real bottleneck isn’t AI’s capability—it’s your judgment. This article breaks down the four levels of judgment, five sources it comes from, and concrete methods to develop it.
「學會 Prompt 就能駕馭 AI」這句話誤導了很多人。真正的瓶頸不是 AI 的能力，是你的判斷力。這篇文章拆解判斷力的四個層次、五個來源，以及具體的培養方法。
GitHub Copilot, Cursor, and Claude Code each have different positioning. But even with the right tool, your productivity might still drop—the key isn’t the tool, it’s whether you have the judgment to wield it. This article analyzes real scenarios for AI-assisted development, common problems, and what kind of people can truly use these tools well.
GitHub Copilot、Cursor、Claude Code 各有定位。但工具選對了，效率還是可能變差——關鍵不在工具，在於你有沒有「判斷力」來駕馭它。這篇文章分析 AI 輔助開發的真實場景、常見問題，以及什麼樣的人能真正用好這些工具。
AI excels at executing tests, but struggles with judging ‘what to test’ and ‘whether results are correct.’ QA’s core value is shifting from execution to acceptance—validating AI’s validation. This article analyzes the four most important QA skills in the AI era and the short, medium, and long-term impacts on teams.
AI 擅長執行測試，但不擅長判斷「該測什麼」和「結果對不對」。QA 的核心價值正在從執行轉向驗收——驗證 AI 的驗證。本文分析 AI 時代 QA 最重要的四種能力排序，以及對團隊的短中長期影響。
After adopting AI, your boss asks about the results—and you can’t give a straight answer. It’s not because AI doesn’t work. It’s because no one defined what ‘success’ looks like from the start. This article provides a practical acceptance framework: four metrics you can start tracking today, and how to establish a baseline when you have no historical data.
導入 AI 後，老闆問成效如何，你答不出來。不是因為 AI 沒用，而是從一開始就沒有定義「成功」長什麼樣。這篇文章提供一個務實的驗收框架：四個今天就能開始追蹤的指標，以及如何在沒有歷史數據的情況下建立比較基準。
深度解析如何利用 LangChain4j、GPT-4o 與 Playwright 打造 AI 測試代理人。本文詳細探討在缺乏文檔的大型 Java 遺留系統中，如何透過「探索、診斷、穩定性」三重迴圈機制，實現超越傳統自動化的自主驗收測試，並提供完整的實作代碼與導入路線圖。
A complete technical implementation plan: Building an AI Testing Agent using LangChain4j, GPT-4o, and Playwright. Includes full code concepts, prompt engineering, state machine architecture, CI/CD integration, and a concrete adoption roadmap.