[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-prompt-engineering-explained-without-the-hype-zh":3,"tags-prompt-engineering-explained-without-the-hype-zh":32,"related-lang-prompt-engineering-explained-without-the-hype-zh":45,"related-posts-prompt-engineering-explained-without-the-hype-zh":49,"series-tools-13819f2d-e9a1-4af2-88f3-7dbe4cb4ce61":86},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":20,"translated_content":10,"views":21,"is_premium":22,"created_at":23,"updated_at":23,"cover_image":11,"published_at":24,"rewrite_status":25,"rewrite_error":10,"rewritten_from_id":26,"slug":27,"category":28,"related_article_id":29,"status":30,"google_indexed_at":31,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":10,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":22},"13819f2d-e9a1-4af2-88f3-7dbe4cb4ce61","別把 Prompt Engineering 想太神","\u003Cp>生成式 AI 很會接話。可是一個字丟給它，常常只會拿到一段空話。AWS 說，prompt engineering 就是用更清楚的指令，去導引模型吐出更有用的答案。\u003C\u002Fp>\u003Cp>講白了，這是把模糊需求，改成可用輸出。對開發者來說，這不是玄學。這是把 LLM 從「會講」變成「能用」的基本功。\u003C\u002Fp>\u003Ch2>Prompt engineering 到底是什麼\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fwhat-is\u002Fprompt-engineering\u002F\" target=\"_blank\" rel=\"noopener\">AWS\u003C\u002Fa> 對 prompt engineering 的定義很直接。它就是用輸入內容，引導生成式 AI 產生想要的結果。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164736525-0uq1.png\" alt=\"別把 Prompt Engineering 想太神\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>翻成台灣話，就是你怎麼跟模型講話，模型就怎麼回你。你給它角色、任務、限制、格式，它通常就比較不會亂飄。你只丟一句「幫我整理」，它很可能回你一坨看似有內容的文字。\u003C\u002Fp>\u003Cp>這件事的本質，不是文筆好不好。重點是結構。你要讓模型知道，自己是誰，要做什麼，要給誰看，最後要長什麼樣子。\u003C\u002Fp>\u003Cp>實務上，好的 prompt 常會包含這幾項：\u003C\u002Fp>\u003Cul>\u003Cli>角色設定，例如客服、工程師、分析師\u003C\u002Fli>\u003Cli>任務範圍，例如摘要、翻譯、分類、寫 code\u003C\u002Fli>\u003Cli>限制條件，例如字數、語氣、禁止內容\u003C\u002Fli>\u003Cli>輸出格式，例如表格、條列、JSON\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這些東西看起來很瑣碎。可是對 LLM 來說，差很多。模型是根據 token 機率往下接，不是人類那種先理解再思考。\u003C\u002Fp>\u003Cp>所以同一個模型，換個 prompt，表現就可能差一截。這也是為什麼 prompt engineering 會變成 AI 應用裡很實際的一環。\u003C\u002Fp>\u003Ch2>為什麼 prompt 會差這麼多\u003C\u002Fh2>\u003Cp>問題不在模型不會答。問題在它太容易答歪。你給的上下文越少，它就越愛自己補腦。這在 demo 階段還好，真的上線就很煩。\u003C\u002Fp>\u003Cp>像 AWS 舉的例子很實際。使用者問「哪裡可以買襯衫」，這句話本身太空。是要網購，還是找附近門市？是男裝、女裝，還是童裝？如果 prompt 沒把條件寫進去，模型只能猜。\u003C\u002Fp>\u003Cp>我覺得這也是很多 AI 產品卡住的地方。團隊以為是模型不夠強，其實是輸入太爛。你要模型穩，先把 prompt 寫穩。\u003C\u002Fp>\u003Cblockquote>“Prompt engineering is the process where you guide generative artificial intelligence solutions to generate desired outputs.” — \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fwhat-is\u002Fprompt-engineering\u002F\" target=\"_blank\" rel=\"noopener\">AWS\u003C\u002Fa>\u003C\u002Fblockquote>\u003Cp>這句話很平，但很準。prompt 不是包裝紙。它就是控制面板。你想要什麼答案，最好先把規則講清楚。\u003C\u002Fp>\u003Cp>對產品團隊來說，這件事還有一個現實好處。prompt 可以重複使用。你不用每次都從零開始寫。把常見場景做成模板，客服、搜尋、摘要、分類都能共用一套骨架。\u003C\u002Fp>\u003Cul>\u003Cli>降低使用者重試次數\u003C\u002Fli>\u003Cli>讓輸出格式更穩定\u003C\u002Fli>\u003Cli>減少不相關回答\u003C\u002Fli>\u003Cli>讓團隊更好維護 AI 功能\u003C\u002Fli>\u003C\u002Ful>\u003Cp>但別把它想成一次寫好就結束。prompt engineering 本來就要反覆測。改一個字，結果可能差很多。這很像除錯，只是你除的是語意，不是 syntax。\u003C\u002Fp>\u003Ch2>實際產品裡，prompt 怎麼用\u003C\u002Fh2>\u003Cp>AWS 把 prompt engineering 的用途分成幾類。像是專業知識、批判思考、創意發想。這些分類看起來很學院派，但其實對應到很多真實產品。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164733470-evgo.png\" alt=\"別把 Prompt Engineering 想太神\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>例如醫療場景，模型可以根據症狀摘要，幫忙整理可能方向。客服場景，prompt 可以要求模型只根據政策回答，不准自由發揮。內容工具則可以把語氣鎖成正式、口語，或偏技術寫法。\u003C\u002Fp>\u003Cp>這就是 prompt engineering 好玩的地方。它不是只會「聊天」。它可以把同一個模型，調成不同工作模式。差別就在 prompt 寫得夠不夠準。\u003C\u002Fp>\u003Cp>下面幾個對比，最容易看出差異：\u003C\u002Fp>\u003Cul>\u003Cli>「摘要這份文件」很容易變成泛泛而談；加上重點、風險、下一步，就會實用很多\u003C\u002Fli>\u003Cli>「哪裡買襯衫」太空；加上地點、價格帶、通路，就能變成可執行建議\u003C\u002Fli>\u003Cli>數學題如果要求先拆步驟，通常比直接要答案更穩\u003C\u002Fli>\u003Cli>創意 brief 如果寫明受眾、情緒、格式，產出通常更接近需求\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這裡要講白一點。prompt engineering 不會讓模型變聰明。它只是讓模型更容易被控制。這個差別很重要，因為你在分配工程時間時，就知道該修模型，還是修輸入。\u003C\u002Fp>\u003Ch2>AWS 提到的幾種 prompting 方法\u003C\u002Fh2>\u003Cp>AWS 列了幾種常見方法。像 chain-of-thought、tree-of-thought、maieutic、complexity-ba\u003Ca href=\"\u002Fnews\u002Ffive-ai-infra-frontiers-bessemer-2026-zh\">se\u003C\u002Fa>d、generated knowledge、least-to-most、self-refine。名字很長，概念其實很直白。\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fmachine-learning\u002Fintroducing-amazon-bedrock\u002F\" target=\"_blank\" rel=\"noopener\">Amazon Bedrock\u003C\u002Fa> 是 AWS 做生成式 AI 應用的服務。這些 prompt 技巧放進去，就很像給 foundation model 裝上不同操作模式。你不是只跟模型聊天，你是在設計它怎麼想。\u003C\u002Fp>\u003Cp>幾個常見方法，可以這樣理解：\u003C\u002Fp>\u003Cul>\u003Cli>chain-of-thought：把問題拆成小步驟\u003C\u002Fli>\u003Cli>tree-of-thought：同時試幾條路，再選一條\u003C\u002Fli>\u003Cli>generated knowledge：先產出相關知識，再拿去回答\u003C\u002Fli>\u003Cli>least-to-most：先解簡單子題，再往上疊\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這些方法的目的，不是炫技。是減少模型亂猜。尤其在多步推理、分析、規則很多的任務上，效果通常比較穩。\u003C\u002Fp>\u003Cp>如果你在做產品，這種穩定性很重要。因為使用者不在乎你的 prompt 多漂亮。他只在乎答案對不對，格式有沒有跑掉，能不能直接拿去用。\u003C\u002Fp>\u003Cp>這裡也可以順手比一下不同 AI 產品的思路。像 \u003Ca href=\"https:\u002F\u002Fopenai.com\u002Fchatgpt\u002F\" target=\"_blank\" rel=\"noopener\">ChatGPT\u003C\u002Fa> 偏通用對話，\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude\" target=\"_blank\" rel=\"noopener\">Claude\u003C\u002Fa> 很強調長文與上下文，\u003Ca href=\"https:\u002F\u002Fwww.google.com\u002Fgemini\u002F\" target=\"_blank\" rel=\"noopener\">Gemini\u003C\u002Fa> 則和 Google 生態整合很深。底層都是模型，但 prompt 寫法會直接影響體驗。\u003C\u002Fp>\u003Ch2>這件事為什麼現在更重要\u003C\u002Fh2>\u003Cp>幾年前，大家還在拼模型參數。現在很多團隊發現，光有大模型不夠。真正上線後，常常是 prompt、資料、流程、權限一起決定結果。\u003C\u002Fp>\u003Cp>這也解釋了為什麼 prompt engineering 會從「技巧」變成「工程」。你要版本控管，要測試，要比對輸出。最好還要能回溯，知道哪版 prompt 讓客服回答開始亂飄。\u003C\u002Fp>\u003Cp>產業脈絡也很明顯。企業導入 AI 後，第一個痛點通常不是模型太弱，而是輸出不穩。今天回得像樣，明天又開始胡扯。這時候，調 prompt 往往比換模型更快。\u003C\u002Fp>\u003Cp>如果你要做更完整的 AI 系統，通常還會搭配 \u003Ca href=\"https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ffunction-calling\" target=\"_blank\" rel=\"noopener\">function calling\u003C\u002Fa>、\u003Ca href=\"\u002Fnews\u002Fwhat-openrag-does-for-enterprise-ai-zh\">RAG\u003C\u002Fa>、權限控管，還有輸出驗證。prompt 只是第一層，但它常常是最先出問題的一層。\u003C\u002Fp>\u003Cp>所以我會說，prompt engineering 不是浪漫的創作。它比較像工程現場的校正工作。很土，但很有效。\u003C\u002Fp>\u003Ch2>結語：先把 prompt 當成程式碼\u003C\u002Fh2>\u003Cp>如果你現在有在做 AI 產品，我會建議一件事：把 prompt 當成程式碼管理。版本要留。測試要做。常見場景要模板化。\u003C\u002Fp>\u003Cp>下一步也很明確。不要只看模型會不會答。要看它在 \u003Ca href=\"\u002Fnews\u002Fmeta-10b-el-paso-ai-data-center-plan-zh\">100\u003C\u002Fa> 次測試裡，能不能穩定輸出同一種格式。能不能少講廢話。能不能守住限制條件。\u003C\u002Fp>\u003Cp>說真的，這才是 prompt engineering 的價值。不是把 AI 變神。是讓它少出包，讓你少收爛尾。\u003C\u002Fp>\u003Cp>如果你的團隊還在手動複製貼上 prompt，現在就該整理了。先從 3 個高頻場景開始。把角色、任務、格式寫死。再看結果有沒有真的穩下來。\u003C\u002Fp>","Prompt engineering 不是玄學。AWS 直接把方法、用途和取捨講清楚，重點是把模糊需求變成可用輸出，讓 LLM 更穩、更好控。","aws.amazon.com","https:\u002F\u002Faws.amazon.com\u002Fwhat-is\u002Fprompt-engineering\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164736525-0uq1.png",[13,14,15,16,17,18,19],"prompt engineering","AWS","生成式 AI","LLM","prompt","Amazon 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會員互通不是「買一次全設備通用」","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778789450987-advz.png","2026-05-14T20:10:24.048988+00:00",{"id":57,"slug":58,"title":59,"cover_image":60,"image_url":60,"created_at":61,"category":28},"7a1e174f-746b-4e82-a0e3-b2475ab39747","why-buns-zig-to-rust-experiment-is-right-zh","為什麼 Bun 的 Zig-to-Rust 實驗是對的","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778767879127-5dna.png","2026-05-14T14:10:26.886397+00:00",{"id":63,"slug":64,"title":65,"cover_image":66,"image_url":66,"created_at":67,"category":28},"e742fc73-5a65-4db3-ad17-88c99262ceb7","why-openai-api-pricing-is-product-strategy-zh","為什麼 OpenAI API 定價是產品策略，不是註腳","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778749859485-chvz.png","2026-05-14T09:10:26.003818+00:00",{"id":69,"slug":70,"title":71,"cover_image":72,"image_url":72,"created_at":73,"category":28},"c757c5d8-eda9-45dc-9020-4b002f4d6237","why-claude-code-prompt-design-beats-ide-copilots-zh","為什麼 Claude Code 的提示設計贏過 IDE Copilot","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778742645084-dao9.png","2026-05-14T07:10:29.371901+00:00",{"id":75,"slug":76,"title":77,"cover_image":78,"image_url":78,"created_at":79,"category":28},"4adef3ab-9f07-4970-91cf-77b8b581b348","why-databricks-model-serving-is-right-default-zh","為什麼 Databricks Model Serving 是生產推論的正確預設","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778692245329-a2wt.png","2026-05-13T17:10:30.659153+00:00",{"id":81,"slug":82,"title":83,"cover_image":84,"image_url":84,"created_at":85,"category":28},"b3305057-451d-48e4-9fb9-69215f7effad","why-ibm-bob-right-kind-ai-coding-assistant-zh","為什麼 IBM 的 Bob 才是對的 AI 寫碼助手","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778664653510-64hc.png","2026-05-13T09:30:21.881547+00:00",[87,92,97,102,107,112,117,122,127,132],{"id":88,"slug":89,"title":90,"created_at":91},"de769291-4574-4c46-a76d-772bd99e6ec9","googles-biggest-gemini-launches-in-2026-zh","Google 2026 最大 Gemini 盤點","2026-03-26T07:26:39.21072+00:00",{"id":93,"slug":94,"title":95,"created_at":96},"855cd52f-6fab-46cc-a7c1-42195e8a0de4","surepath-real-time-mcp-policy-controls-zh","SurePath 推出即時 MCP 政策控管","2026-03-26T07:57:40.77233+00:00",{"id":98,"slug":99,"title":100,"created_at":101},"9b19ab54-edef-4dbd-9ce4-a51e4bae4ebb","mcp-in-2026-the-ai-tool-layer-teams-use-zh","2026 年 MCP：團隊真的在用的 AI 工具層","2026-03-26T08:01:46.589694+00:00",{"id":103,"slug":104,"title":105,"created_at":106},"af9c46c3-7a28-410b-9f04-32b3de30a68c","prompting-in-2026-what-actually-works-zh","2026 提示工程，真正有用的是什麼","2026-03-26T08:08:12.453028+00:00",{"id":108,"slug":109,"title":110,"created_at":111},"05553086-6ed0-4758-81fd-6cab24b575e0","garry-tan-open-sources-claude-code-toolkit-zh","Garry Tan 開源 Claude Code 工具包","2026-03-26T08:26:20.068737+00:00",{"id":113,"slug":114,"title":115,"created_at":116},"042a73a2-18a2-433d-9e8f-9802b9559aac","github-ai-projects-to-watch-in-2026-zh","2026 必看 20 個 GitHub AI 專案","2026-03-26T08:28:09.619964+00:00",{"id":118,"slug":119,"title":120,"created_at":121},"a5f94120-ac0d-4483-9a8b-63590071ac6a","claude-code-vs-cursor-2026-zh","Claude Code 與 Cursor 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