[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-why-prompt-engineering-is-wrong-about-2026-zh":3,"article-related-why-prompt-engineering-is-wrong-about-2026-zh":31,"series-research-4a829d2a-24a3-42dd-8be4-49e5ab35435a":85},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"4a829d2a-24a3-42dd-8be4-49e5ab35435a","why-prompt-engineering-is-wrong-about-2026-zh","為什麼 2026 年 prompt engineering 錯了","\u003Cp data-speakable=\"summary\">2026 年決定 AI 輸出品質的，不是 prompt 技巧，而是 context engineering。\u003C\u002Fp>\u003Cp>\u003Ca href=\"\u002Ftag\u002Fprompt-engineering\">Prompt engineering\u003C\u002Fa> 已經不是區分好壞輸出的主技能；context engineering 才是。研究與實務都在指向同一件事：當你把角色、任務、格式、範例、限制與工具輸入設計好，模型表現會明顯穩定。相反地，靠一句「幫我寫得好一點」去碰運氣，通常只會得到不可重複的結果。\u003C\u002Fp>\u003Ch2>第一個論點：結構比即興更重要\u003C\u002Fh2>\u003Cp>語言模型不是照人類直覺推理，而是根據你給的上下文預測下一個 \u003Ca href=\"\u002Ftag\u002Ftoken\">token\u003C\u002Fa>。這代表 prompt 不是咒語，而是規格書。當你明確定義角色、任務、輸出格式、受眾與限制，你不是在修飾指令，而是在消除歧義；歧義，才是錯誤輸出的主要來源。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780661884287-ow45.png\" alt=\"為什麼 2026 年 prompt engineering 錯了\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>最簡單的框架已經證明這點。像 RTF、TAG、TASS 這類方法之所以有效，是因為它們迫使使用者先做決策，再交給模型執行。一句「你是運動營養師，請產出 7 天菜單，並以 HTML 表格輸出」不只是比隨口要求更清楚，它也更便宜、更容易重現，而且更不容易跑題。這就是為\u003Ca href=\"\u002Fnews\u002Fwhy-anthropic-is-right-ai-successors-zh\">什麼\u003C\u002Fa>這些框架能覆蓋大多數日常場景。\u003C\u002Fp>\u003Ch2>第二個論點：範例比聰明措辭更有用\u003C\u002Fh2>\u003Cp>研究早就把答案講明白了。Brown 等人在 2020 年的 few-shot learning 工作中發現，給 GPT-3 提供 10 到 100 個範例後，它在特定任務上的表現可以逼近甚至超過微調模型。這件事打破了一個迷思：提升輸出品質的關鍵，不一定是更強的訓練，而是更好的示例。\u003C\u002Fp>\u003Cp>所以 CARE 與 CREATE 這類框架才實用。CARE 的最後一個 E 是 Example，意思很直接：把抽象要求\u003Ca href=\"\u002Fnews\u002Frisc-v-news-chip-tracking-playbook-zh\">變成\u003C\u002Fa>可模仿的模式。CREATE 更進一步，把 additions、extras、negative constraints 都納進來。實務上，「不要提競品」「避免誇大」「不要用反問開場」這些負向限制，常常比一整頁正向指令更有效。這不是吹毛求疵，而是控制。\u003C\u002Fp>\u003Ch2>第二個論點：複雜任務需要推理框架，不是更花俏的 prompt\u003C\u002Fh2>\u003Cp>一旦任務變複雜，輕量框架就不夠了。Chain-of-Thought、Tree of Thoughts、ReAct、Self-Refine 之所以存在，是因為它們逼模型把中間步驟攤開，而不是一次亂猜。數字很直接：在 Game of 24 基準上，標準 CoT 只解出 4%，Tree of Thoughts 卻拉到 74%。這不是小幅改善，而是從玩具變工具。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780661871427-qdcc.png\" alt=\"為什麼 2026 年 prompt engineering 錯了\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>ReAct 更清楚地說明，這個領域早就超過單純 prompt 技巧。當模型要搭配搜尋、計算、驗證與外部工具時，靜態文字回答的上限很低。現在很多 \u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> 工作流都建立在這種模式上。若你的場景涉及檢索、工具或多步決策，prompt 只是第一層；真正決定成敗的是 context pipeline。\u003C\u002Fp>\u003Ch2>反方可能怎麼說\u003C\u002Fh2>\u003Cp>舊式 prompt engineering 最強的辯護是，它仍然是最快取得價值的方法。對行銷人來說，寫文案；對創辦人來說，整理會議紀錄；對工程師來說，產生一段程式碼，好的 RTF prompt 往往就夠用。多數人也不需要 agent stack、retrieval layer 或程序化編排。他們只需要一個乾淨的請求與穩定的格式。\u003C\u002Fp>\u003Cp>這個說法並沒有錯，但只適用於一次性任務。當輸出必須可重複、可稽核、可嵌入產品時，「prompting」這個詞就太小了。context engineering \u003Ca href=\"\u002Fnews\u002Frisc-v-gpu-pairing-right-soc-bet-zh\">才是正確\u003C\u002Fa>名稱，因為真正的工作是選擇輸入、範例、工具、限制、記憶與檢索，讓模型可靠地做事。Prompting 是戰術，context engineering 才是系統。\u003C\u002Fp>\u003Ch2>你能做什麼\u003C\u002Fh2>\u003Cp>如果你是工程師，別再優化花俏措辭，改為優化輸入設計：定義 schema、加入範例、加上負向限制，並在需要事實或動作時接入檢索或工具。如果你是 PM，把 prompts 當成介面來設計，並用一致性、延遲、失敗模式與使用者信任來衡量品質。如果你是創辦人，把 AI 功能建立在 context pipeline 上，而不是 prompt library 上。到了 2026 年，贏的團隊不是 prompt 模板最炫的團隊，而是最懂得提供上下文的團隊。\u003C\u002Fp>","2026 年真正決定 AI 輸出品質的不是 prompt 技巧，而是 context engineering；結構化輸入、範例與工具串接，才是降低錯誤與提升可重複性的關鍵。","pasqualepillitteri.it","https:\u002F\u002Fpasqualepillitteri.it\u002Fen\u002Fnews\u002F1090\u002Fprompt-engineering-2026-frameworks-complete-guide",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780661884287-ow45.png","research","zh","aadc9843-d668-4507-8c2b-5eea7f352bb6",[17,18,19,20,21,22],"prompt engineering","context engineering","few-shot learning","Chain-of-Thought","ReAct","Tree of Thoughts",[24,25,26],"prompt engineering 已不足以解釋 2026 年的 AI 成效，context engineering 才是核心","結構、範例與負向限制，比聰明措辭更能降低錯誤並提升可重複性","複雜任務要靠 context pipeline 與推理框架，而不是單一 prompt 技巧",0,"2026-06-05T12:17:19.813402+00:00","2026-06-05T12:17:19.789+00:00","0c35a120-52fc-41fc-afa3-d404eb934158",{"tags":32,"relatedLang":44,"relatedPosts":48},[33,35,37,39,42],{"name":17,"slug":34},"prompt-engineering",{"name":19,"slug":36},"few-shot-learning",{"name":18,"slug":38},"context-engineering",{"name":40,"slug":41},"React","react",{"name":43,"slug":43},"chain-of-thought",{"id":15,"slug":45,"title":46,"language":47},"why-prompt-engineering-is-wrong-about-2026-en","Why Prompt Engineering Is Wrong About 2026","en",[49,55,61,67,73,79],{"id":50,"slug":51,"title":52,"cover_image":53,"image_url":53,"created_at":54,"category":13},"52a37532-880d-4261-8f62-2f254d6c592d","spire-evidence-grounded-ai-humanities-zh","SPIRE 讓人文 AI 更重證據","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780647483844-bcuj.png","2026-06-05T08:17:29.603104+00:00",{"id":56,"slug":57,"title":58,"cover_image":59,"image_url":59,"created_at":60,"category":13},"b38c56a6-e7f3-45fb-b100-d37e7b3ed417","reinforcement-aware-distillation-llm-reasoning-zh","強化感知蒸餾，想把推理一起學進去","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780646589500-0me6.png","2026-06-05T08:02:33.908932+00:00",{"id":62,"slug":63,"title":64,"cover_image":65,"image_url":65,"created_at":66,"category":13},"60f7d702-20a7-4cec-9a80-185f072c8dfe","next-token-models-plan-ahead-zh","次詞模型其實會先想一步","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780645684780-roea.png","2026-06-05T07:47:34.35089+00:00",{"id":68,"slug":69,"title":70,"cover_image":71,"image_url":71,"created_at":72,"category":13},"7ec803f7-2658-4c9e-baa6-2b8528407d7f","google-deepmind-co-scientist-researchers-zh","Google DeepMind 對外開放 Co-Scientist","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780636679231-q694.png","2026-06-05T05:17:30.68789+00:00",{"id":74,"slug":75,"title":76,"cover_image":77,"image_url":77,"created_at":78,"category":13},"923bb0c4-95f3-49a0-8e01-5cdd6bcd2e32","fixing-llm-forgetting-es-fine-tuning-zh","ES 微調忘記問題有解了","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780604276240-arx4.png","2026-06-04T20:17:25.720929+00:00",{"id":80,"slug":81,"title":82,"cover_image":83,"image_url":83,"created_at":84,"category":13},"42510df4-4692-44c6-a45a-c82a4a86b646","tls-turns-insecure-links-into-encrypted-sessions-zh","TLS 把明文連線變成加密會話","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780596207456-9or4.png","2026-06-04T18:02:50.988357+00:00",[86,91,96,101,106,111,116,121,126,131],{"id":87,"slug":88,"title":89,"created_at":90},"f18dbadb-8c59-4723-84a4-6ad22746c77a","deepmind-bets-on-continuous-learning-ai-2026-zh","DeepMind 押注 2026 連續學習 AI","2026-03-26T08:16:02.367355+00:00",{"id":92,"slug":93,"title":94,"created_at":95},"f4a106cb-02a6-4508-8f39-9720a0a93cee","ml-papers-of-the-week-github-research-desk-zh","每週 ML 論文清單，為何紅到 GitHub","2026-03-27T01:11:39.284175+00:00",{"id":97,"slug":98,"title":99,"created_at":100},"c4f807ca-4e5f-47f1-a48c-961cf3fc44dc","ai-ml-conferences-to-watch-in-2026-zh","2026 AI 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