[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-rag":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"2286390e-c09c-48fd-8f28-8dc871fb9603","RAG","rag",19,"RAG（Retrieval-Augmented Generation）把檢索與生成接在一起，讓模型先找資料再回答，常見於企業知識庫、語意搜尋、客服與研究助理。這裡聚焦向量資料庫、混合搜尋、索引與召回評估等實作議題。","RAG, or retrieval-augmented generation, combines search with model output so answers can be grounded in source data. This tag covers vector databases, hybrid search, indexing, recall, and evaluation for knowledge bases, semantic search, and enterprise assistants.",[12,21,29,37,44,52,59,66,73,80,87,94,101,108,115,122,129,136,143,150,157,164,171,178,185,192,199,206,213,220,227,234,241,248],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"7095f05c-34f5-469f-a044-2525d2010ce9","how-to-add-temporal-rag-in-production-zh","如何在正式環境加入 Temporal RAG","這篇教你在既有 RAG 中加入時間感知重排層，讓新版本、有效期間內的事件與最新資料優先被 LLM 使用。","ai-agent","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778667053844-osvs.png","zh","2026-05-13T10:10:30.930982+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":26,"image_url":27,"cover_image":27,"language":19,"created_at":28},"cec2d028-df49-4444-a0e2-e857109414bf","longmemeval-v2-agent-memory-web-workflows-zh","LongMemEval-V2：測 agent 長期記憶","LongMemEval-V2 用 451 題測試 agent 能否記住 Web 環境經驗，而不只是使用者歷史；結果顯示以 coding agent 蒐證的記憶法準確率最高，但延遲也更高。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778653249376-2wo2.png","2026-05-13T06:20:29.320872+00:00",{"id":30,"slug":31,"title":32,"summary":33,"category":34,"image_url":35,"cover_image":35,"language":19,"created_at":36},"e60e5a80-ca58-46fb-be85-5d51d7f3d2df","ragflow-open-source-rag-agent-engine-zh","RAGFlow 加入 Agent 與自架部署","RAGFlow 把開源 RAG、Agent、自架部署和新模型支援整合在一起，適合處理 PDF、表格和多來源文件的團隊。","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778526664887-ynxt.png","2026-05-11T19:10:35.308559+00:00",{"id":38,"slug":39,"title":40,"summary":41,"category":17,"image_url":42,"cover_image":42,"language":19,"created_at":43},"e63f8dd8-b563-4db4-987e-2118469bc8a7","why-pinecone-compiled-vector-artifacts-ai-agents-zh","為什麼 Pinecone 的編譯式向量工件才是 AI agents 的正解","Pinecone 的方向是對的：AI agents 需要先編譯好的知識工件，而不是每次即時翻找原始向量。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778328644209-kopr.png","2026-05-09T12:10:22.152878+00:00",{"id":45,"slug":46,"title":47,"summary":48,"category":49,"image_url":50,"cover_image":50,"language":19,"created_at":51},"27143bae-96b1-4a33-9906-0b546a29df2c","why-rag-in-microsoft-foundry-needs-better-indexes-zh","為什麼 Microsoft Foundry 的 RAG 需要更好的索引，不需要…","Microsoft Foundry 的 RAG 成敗關鍵在索引與檢索品質，不在把提示詞越寫越長。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778300458830-etqy.png","2026-05-09T04:20:23.667583+00:00",{"id":53,"slug":54,"title":55,"summary":56,"category":49,"image_url":57,"cover_image":57,"language":19,"created_at":58},"1ccc7e60-55db-42a3-8318-34976673d3b7","chatgpt-goblin-bug-closed-models-fragile-zh","為什麼 ChatGPT 的 goblin bug 證明封閉模型太脆弱","ChatGPT 的 goblin bug 說明，封閉式 LLM 若無法被外部審計與約束，就不適合當作嚴肅生產系統的底層基礎。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778289045602-ikm9.png","2026-05-09T01:10:22.723975+00:00",{"id":60,"slug":61,"title":62,"summary":63,"category":17,"image_url":64,"cover_image":64,"language":19,"created_at":65},"05d8ff3d-05df-4648-9117-ee32decd5a00","how-to-build-advanced-rag-in-n8n-zh","怎麼做 n8n 進階 RAG","這篇教你在 n8n 裡做一條可上線的進階 RAG 流程，包含切塊、混合檢索、重排序與壓縮。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778209857169-u9xd.png","2026-05-08T03:10:29.599439+00:00",{"id":67,"slug":68,"title":69,"summary":70,"category":17,"image_url":71,"cover_image":71,"language":19,"created_at":72},"a8e2e21f-b0d2-4f4f-89bb-1936d5fe8fd5","how-to-build-agentic-rag-with-langgraph-zh","如何用 LangGraph 打造 Agentic RAG","這篇教你用 LangGraph 建立一個會路由、檢索、驗證並回答問題的 Agentic RAG 工作流。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778120450823-zxhl.png","2026-05-07T02:20:28.380469+00:00",{"id":74,"slug":75,"title":76,"summary":77,"category":17,"image_url":78,"cover_image":78,"language":19,"created_at":79},"261fa342-f7bb-4330-a97a-a95f10ae3f94","why-rag-is-ending-for-agentic-ai-zh","為什麼 RAG 正在結束，agentic AI 需要的是編譯式知識層","RAG 不再適合作為 agentic AI 的預設架構，因為代理需要可重用、可驗證的編譯式知識層，而不是每一步都重新檢索原始文本。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778105450961-s3p1.png","2026-05-06T22:10:28.570639+00:00",{"id":81,"slug":82,"title":83,"summary":84,"category":26,"image_url":85,"cover_image":85,"language":19,"created_at":86},"eeeff79e-4789-40ce-a55d-dba97d54ada2","why-rag-needs-self-healing-layer-zh","為什麼 RAG 需要自癒層，而不只是更好的提示詞","RAG 應被視為會失敗的系統，真正該補的是即時自癒層，而不是繼續迷信提示詞調校。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778098242230-wbbc.png","2026-05-06T20:10:22.158933+00:00",{"id":88,"slug":89,"title":90,"summary":91,"category":26,"image_url":92,"cover_image":92,"language":19,"created_at":93},"bf5e8812-6fcc-4509-88fa-471708fb8e7c","why-open-source-llms-should-be-judged-by-workload-not-hype-zh","為什麼開源 LLM 應該按工作負載來選，不該看熱度","2026 年選開源 LLM，應該先看工作負載是否匹配，而不是追逐排行榜與發布熱度。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778095237993-2zi1.png","2026-05-06T19:20:21.620944+00:00",{"id":95,"slug":96,"title":97,"summary":98,"category":26,"image_url":99,"cover_image":99,"language":19,"created_at":100},"92b08177-95c6-4743-89a9-f0314e6359c9","retrieval-augmented-generation-explained-zh","RAG 是什麼？白話看懂","RAG 讓 LLM 先查文件再回答，能減少幻覺、補上引用，也更適合企業知識庫與即時資料。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778083864937-hhfs.png","2026-05-06T16:10:33.474941+00:00",{"id":102,"slug":103,"title":104,"summary":105,"category":34,"image_url":106,"cover_image":106,"language":19,"created_at":107},"8299ded2-e180-43cf-b78e-96ac23033d26","paperless-ai-document-chat-rag-hybrid-search-zh","Paperless-AI：把文件庫變聊天機器人","Paperless-AI 把 Paperless-ngx 變成可聊天的文件庫，結合 RAG、hybrid search、AI 標籤與自架部署，適合大量合約、發票與內部文件。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778062851674-apws.png","2026-05-06T10:20:34.644825+00:00",{"id":109,"slug":110,"title":111,"summary":112,"category":26,"image_url":113,"cover_image":113,"language":19,"created_at":114},"f138a001-0992-4842-9a06-325d30fc6004","rag-precision-tuning-hurts-retrieval-accuracy-zh","RAG 精準調校反而害檢索","Redis 研究指出，RAG embedding 若只追求 precision，檢索準確率可能掉 40%，還會拖累 agentic pipeline。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778055657010-r5a0.png","2026-05-06T08:20:36.321486+00:00",{"id":116,"slug":117,"title":118,"summary":119,"category":17,"image_url":120,"cover_image":120,"language":19,"created_at":121},"7a9fa13f-1fbb-438f-bdc7-c47cc5cd1dae","agentic-ai-moving-past-rag-knowledge-layer-zh","Agentic AI 為何開始跳過 RAG","Agentic AI 正從 RAG 轉向預先編譯的知識層，重點是減少推理時重複讀資料、降 token 成本，讓多步驟代理更好控。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778055061154-hqfw.png","2026-05-06T08:10:37.387055+00:00",{"id":123,"slug":124,"title":125,"summary":126,"category":34,"image_url":127,"cover_image":127,"language":19,"created_at":128},"ca4809be-9913-404d-aea5-53bb3b41c786","aws-bedrock-knowledge-bases-rag-zh","AWS Bedrock Knowledge Bases 怎麼簡化 RAG","AWS Bedrock Knowledge Bases 把 RAG 的擷取、向量庫、重排序和引用整合成託管服務，適合要接企業內部資料的 AI 應用。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777960285938-buk7.png","2026-05-05T05:51:07.759842+00:00",{"id":130,"slug":131,"title":132,"summary":133,"category":49,"image_url":134,"cover_image":134,"language":19,"created_at":135},"94616438-b26b-4ff5-a98f-6add5b4765e4","why-databricks-rag-is-platform-play-not-feature-zh","為什麼 Databricks 的 RAG 是平台戰，不是功能","Databricks 把 RAG 當成端到端平台問題，這不是包裝，而是正確的產品判斷。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777959647452-yykk.png","2026-05-05T05:40:27.168734+00:00",{"id":137,"slug":138,"title":139,"summary":140,"category":17,"image_url":141,"cover_image":141,"language":19,"created_at":142},"e133ed69-fb56-495d-96f6-1e14d7ac3242","how-to-build-a-rag-pipeline-in-5-steps-zh","5 步完成 RAG 管線","這篇教你用 5 個步驟做出 RAG 管線，讓模型先檢索你的文件，再根據內容產生有依據的答案。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777959047822-j4yr.png","2026-05-05T05:30:30.368078+00:00",{"id":144,"slug":145,"title":146,"summary":147,"category":26,"image_url":148,"cover_image":148,"language":19,"created_at":149},"254c9611-aa49-4f96-be03-77c9c2f8007b","what-rag-is-and-why-it-matters-zh","RAG 是什麼，為何重要","RAG 讓 LLM 先查外部可信資料再回答，能降低幻覺、更新更快，也更適合企業文件與權限控管。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777958450449-qp57.png","2026-05-05T05:20:30.928679+00:00",{"id":151,"slug":152,"title":153,"summary":154,"category":34,"image_url":155,"cover_image":155,"language":19,"created_at":156},"d2ba34dd-5488-4b1d-9af8-e38092d7a806","actian-vectorai-db-claims-22x-faster-search-zh","Actian VectorAI DB 主打 22 倍搜尋速度","Actian 推出 VectorAI DB，把向量搜尋塞進資料庫內，主打比常見方案快 22 倍，也想少掉外部向量服務的整合成本。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777936259138-tnh6.png","2026-05-04T23:10:38.752315+00:00",{"id":158,"slug":159,"title":160,"summary":161,"category":49,"image_url":162,"cover_image":162,"language":19,"created_at":163},"f57d2afa-7a99-40c3-870a-06290956b5db","why-2026-ai-engineer-roadmap-wrong-starting-point-zh","為什麼 2026 AI 工程師路線圖不是最佳起點","2026 AI 工程師路線圖太寬，適合當參考，不適合當第一份學習計畫。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777860652350-3s7g.png","2026-05-04T02:10:24.315161+00:00",{"id":165,"slug":166,"title":167,"summary":168,"category":34,"image_url":169,"cover_image":169,"language":19,"created_at":170},"fbd1528b-5af1-4e8c-ab31-1af9ac25fc5c","why-qdrant-cloud-enterprise-push-matters-ai-retrieval-zh","為什麼 Qdrant Cloud 的企業化推進，對 AI 檢索很重要","Qdrant Cloud 把向量檢索做成企業級基礎設施，因為 AI 檢索真正需要的是速度、可用性與可稽核性，而不是只會跑 demo。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777859592356-14tq.png","2026-05-04T01:52:57.160921+00:00",{"id":172,"slug":173,"title":174,"summary":175,"category":26,"image_url":176,"cover_image":176,"language":19,"created_at":177},"ac5a1a8a-b0f6-46f6-85f5-47f01b5f6c51","mathnet-benchmark-math-reasoning-retrieval-zh","MathNet 把數學推理和檢索一起測","MathNet 用 30,676 題、47 國、17 語言的奧賽數學題，同時測推理、相似題檢索與 RAG 效果。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776751441331-oooq.png","2026-04-21T06:03:38.63837+00:00",{"id":179,"slug":180,"title":181,"summary":182,"category":34,"image_url":183,"cover_image":183,"language":19,"created_at":184},"0ad0e45d-cb40-4267-bab8-d05ed973896a","qdrant-milvus-weaviate-rag-2026-comparison-zh","2026 RAG 向量資料庫三選一","2026 年做 RAG，Qdrant、Milvus、Weaviate 各有強項。這篇用延遲、規模、混合搜尋、成本與開發體驗，直接比較三者差異。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776126302600-xxf9.png","2026-04-14T00:24:39.218956+00:00",{"id":186,"slug":187,"title":188,"summary":189,"category":34,"image_url":190,"cover_image":190,"language":19,"created_at":191},"4aa53f25-2206-4632-b428-84fc839b9794","redis-vector-search-quick-start-guide-zh","Redis 向量搜尋快速上手","Redis 不只拿來快取。這篇看它怎麼存 embeddings、建索引、跑 KNN 查詢，順手把語意搜尋和 RAG 的實作路徑講清楚。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776126124430-awwk.png","2026-04-14T00:21:38.036845+00:00",{"id":193,"slug":194,"title":195,"summary":196,"category":26,"image_url":197,"cover_image":197,"language":19,"created_at":198},"6510a804-74fd-4073-9c73-a1b4d3dc491c","ibm-100b-vector-database-single-server-zh","IBM 單機塞進 1000 億向量","IBM 宣稱 CAS 原型在單一伺服器上索引 1000 億向量，平均延遲 694 毫秒、召回率超過 90%。這篇拆解它怎麼做、跟一般向量資料庫差在哪、以及對企業 RAG 架構的影響。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776125936277-ct7n.png","2026-04-14T00:18:35.333469+00:00",{"id":200,"slug":201,"title":202,"summary":203,"category":34,"image_url":204,"cover_image":204,"language":19,"created_at":205},"feb9176d-89c6-4bd0-a82a-8440625d8c94","awesome-open-source-ai-projects-list-zh","開源 AI 專案清單怎麼挑","這份 GitHub 清單收錄可直接上線的開源 AI 專案，從 PyTorch 到 vLLM 都有，2,486 顆星，適合想找模型、推理、RAG 和代理工具的工程師。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775999036470-b4zr.png","2026-04-12T13:03:35.795784+00:00",{"id":207,"slug":208,"title":209,"summary":210,"category":34,"image_url":211,"cover_image":211,"language":19,"created_at":212},"663a3bd8-6160-4b37-bf18-e3c54e7541d2","windsurf-flow-context-engine-2026-zh","Windsurf Flow 怎麼讓上下文不斷線","Windsurf Flow 用索引、記憶與規則維持 AI 上下文。本文拆解 Cascade、Tab、RAG 與 .windsurfrules 的運作方式，並比較它和其他 AI 寫碼工具的差異。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775272013033-va0v.png","2026-04-04T03:06:35.776413+00:00",{"id":214,"slug":215,"title":216,"summary":217,"category":49,"image_url":218,"cover_image":218,"language":19,"created_at":219},"a0660205-5b41-49a6-8119-ee9105a7e1f5","chatgpt-ads-format-standardization-data-zh","ChatGPT 廣告越來越一致","40,000 筆廣告版位分析顯示，ChatGPT 廣告正變得更短、更直白、更標準化。這反映 OpenAI 在優化轉換，也透露 LLM 使用習慣正在往任務導向收斂。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775218190861-p9x8.png","2026-04-03T12:09:37.164139+00:00",{"id":221,"slug":222,"title":223,"summary":224,"category":34,"image_url":225,"cover_image":225,"language":19,"created_at":226},"af6bd5c1-b58a-4c4c-adff-9dd6a2bf7cbe","ferresdb-production-rust-vector-db-updates-zh","FerresDB 走向正式上線的 Rust 搜尋","FerresDB 新增 PolarQuant、HNSW 自動調參、PITR、reranking 與 Raft 分散式儲存，開始像一套可上線的 Rust 向量資料庫。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775168156597-iqqe.png","2026-04-02T22:15:41.697535+00:00",{"id":228,"slug":229,"title":230,"summary":231,"category":34,"image_url":232,"cover_image":232,"language":19,"created_at":233},"7f3d9a39-815e-49c4-aced-a5454e7b4afa","what-openrag-does-for-enterprise-ai-zh","OpenRAG 在企業 AI 的用途","IBM OpenRAG 把檢索、索引和模型協調包成一套。適合用公司內部資料做 RAG，讓回答更貼近文件，也更好追查來源。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164554067-i4oe.png","2026-04-02T21:15:38.707251+00:00",{"id":235,"slug":236,"title":237,"summary":238,"category":17,"image_url":239,"cover_image":239,"language":19,"created_at":240},"f2ca7720-b471-4ce5-9336-2a9ac2a876fd","amazon-bedrock-agents-multi-agent-workflows-zh","Amazon Bedrock Agents 進入多代理工作流","AWS 為 Amazon Bedrock Agents 加入記憶、程式執行與多代理協作，目標是處理更複雜的企業工作流。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775051177170-9bcp.png","2026-04-01T09:30:29.945429+00:00",{"id":242,"slug":243,"title":244,"summary":245,"category":34,"image_url":246,"cover_image":246,"language":19,"created_at":247},"042a73a2-18a2-433d-9e8f-9802b9559aac","github-ai-projects-to-watch-in-2026-zh","2026 必看 20 個 GitHub AI 專案","OpenClaw 衝到約 30.2 萬 GitHub stars 很吸睛，但 2026 年更大的變化其實是另一件事：開源 AI 焦點正從聊天介面，轉向 agents、工作流程、RAG 與多模態工具。這份名單反映的，是 AI 正在變成可部署的軟體基礎層。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1774519972047-6tx9.png","2026-03-26T08:28:09.619964+00:00",{"id":249,"slug":250,"title":251,"summary":252,"category":49,"image_url":253,"cover_image":253,"language":19,"created_at":254},"fa006110-18df-40ed-ac43-1e2133fa2c06","rag-in-2026-what-enterprise-ai-needs-now-zh","2026 年企業 AI 為何更靠 RAG","RAG 已從展示用技術走進企業預算。原因很直接：公司要的是能讀取最新內部資料、可追溯、可控權限的 AI，而不是只會背舊訓練資料的聊天模型。到了 2026 年，真正有用的重點在檢索品質、權限治理、即時資料連接與合規設計。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1774517341124-yymy.png","2026-03-26T08:06:06.808873+00:00"]