{
 "reviewed": "2026-07-14",
 "disclosure": "ALLinAI 編輯術語表，不是法律定義、技術標準或產品選型結論。",
 "sources": {
  "nist_ai_rmf": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/",
  "taiwan_ai_basic_act": "https://law.nstc.gov.tw/LawContent.aspx?id=GL000592&kw=",
  "taiwan_ai_risk_framework": "https://moda.gov.tw/major-policies/ai/governance/19244",
  "owasp_genai_glossary": "https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/"
 },
 "categories": {
  "basics": "AI 基礎",
  "data-rag": "資料與 RAG",
  "evaluation": "評測與品質",
  "governance": "治理與風險"
 },
 "terms": [
  {
   "slug": "artificial-intelligence",
   "category": "basics",
   "name": "人工智慧",
   "english": "Artificial Intelligence, AI",
   "definition": "能依輸入或感測，透過模型與演算法產生預測、內容、建議或決策等輸出的系統總稱；不同法律與標準的定義範圍可能不同。",
   "why": "先確認某項功能是否真的屬於適用規範中的 AI，不能只看產品行銷名稱。",
   "related": "/knowledge/enterprise-ai/taiwan-ai-basic-act-enterprise-guide/"
  },
  {
   "slug": "machine-learning",
   "category": "basics",
   "name": "機器學習",
   "english": "Machine Learning, ML",
   "definition": "讓系統從資料辨識模式並改善特定任務表現的方法，不代表系統會像人一樣理解或自行負責。",
   "why": "資料、目標與部署環境改變時，原有表現可能不再成立。",
   "related": "/knowledge/enterprise-ai/ai-observability-logging-monitoring/"
  },
  {
   "slug": "generative-ai",
   "category": "basics",
   "name": "生成式 AI",
   "english": "Generative AI",
   "definition": "依提示與既有模型能力產生文字、圖像、音訊、程式或其他內容的 AI。",
   "why": "輸出可能合理但錯誤，也可能涉及敏感資料、權利與揭露問題。",
   "related": "/knowledge/enterprise-ai/generative-ai-use-policy-checklist/"
  },
  {
   "slug": "large-language-model",
   "category": "basics",
   "name": "大型語言模型",
   "english": "Large Language Model, LLM",
   "definition": "以大量語言資料訓練、依上下文預測並生成文字或相關序列的模型。",
   "why": "流暢度不等於事實正確；正式用途需要來源、測試、拒答與人工責任。",
   "related": "/knowledge/ai-agent/what-is-rag-enterprise-knowledge-base/"
  },
  {
   "slug": "ai-agent",
   "category": "basics",
   "name": "AI Agent",
   "english": "AI Agent",
   "definition": "能依目標規劃步驟、使用工具、讀寫外部系統或持續執行任務的 AI 應用。",
   "why": "能採取動作時，權限、批准、上限、日誌、停止與回復比聊天介面更重要。",
   "related": "/knowledge/ai-agent/ai-agent-vs-chatbot-vs-workflow/"
  },
  {
   "slug": "copilot",
   "category": "basics",
   "name": "AI Copilot",
   "english": "AI Copilot",
   "definition": "以建議、草稿、整理或查詢方式輔助人員工作的 AI，不應暗示所有輸出都已由人確認。",
   "why": "要定義人員何時檢查、如何否決，以及誰對正式結果負責。",
   "related": "/ai-use-cases/"
  },
  {
   "slug": "prompt",
   "category": "basics",
   "name": "提示",
   "english": "Prompt",
   "definition": "提供給模型的指令、背景、資料、範例與輸出要求；可能由使用者、系統或流程組合。",
   "why": "提示是系統行為的一部分，需版本化、測試，並避免放入未核准敏感資料。",
   "related": "/knowledge/enterprise-ai/ai-requirements-document-template/"
  },
  {
   "slug": "token",
   "category": "basics",
   "name": "Token",
   "english": "Token",
   "definition": "模型處理文字時使用的切分單位，不固定等於一個中文字、英文字或字元。",
   "why": "上下文限制、延遲與部分用量成本常以 token 計算，估算時需使用實際工具。",
   "related": "/knowledge/enterprise-ai/ai-project-cost-estimation-guide/"
  },
  {
   "slug": "rag",
   "category": "data-rag",
   "name": "檢索增強生成",
   "english": "Retrieval-Augmented Generation, RAG",
   "definition": "回答前先從指定資料來源檢索相關內容，再把檢索結果提供給生成模型的架構。",
   "why": "它能改善資料新鮮度與來源追溯，但不能自動保證檢索正確、權限正確或不會生成錯誤。",
   "related": "/knowledge/ai-agent/what-is-rag-enterprise-knowledge-base/"
  },
  {
   "slug": "embedding",
   "category": "data-rag",
   "name": "向量嵌入",
   "english": "Embedding",
   "definition": "把文字、圖片或其他資料轉成可比較的數值向量，用於相似度搜尋、分群或推薦。",
   "why": "相似不等於正確或有權存取；仍需中繼資料、權限與評測。",
   "related": "/knowledge/ai-agent/enterprise-knowledge-base-access-control-security/"
  },
  {
   "slug": "vector-database",
   "category": "data-rag",
   "name": "向量資料庫",
   "english": "Vector Database",
   "definition": "儲存向量並支援相似度查詢的資料系統，常用於 RAG 檢索。",
   "why": "選型要同時考慮權限、刪除、備份、版本、篩選與營運成本。",
   "related": "/knowledge/enterprise-ai/rag-cost-estimation-guide/"
  },
  {
   "slug": "chunking",
   "category": "data-rag",
   "name": "文件切塊",
   "english": "Chunking",
   "definition": "把文件切成可索引與檢索的片段，通常會搭配標題、來源、版本與其他中繼資料。",
   "why": "切得過大可能混入無關內容，過小則失去上下文，必須用真實問題測試。",
   "related": "/knowledge/ai-agent/enterprise-knowledge-base-document-preparation/"
  },
  {
   "slug": "metadata",
   "category": "data-rag",
   "name": "中繼資料",
   "english": "Metadata",
   "definition": "描述內容的欄位，例如來源、owner、版本、生效日、部門、密等與適用對象。",
   "why": "它支援權限、篩選、更新、引用與追溯，是知識庫治理的核心。",
   "related": "/tools/rag-readiness-check/"
  },
  {
   "slug": "grounding",
   "category": "data-rag",
   "name": "依據約束",
   "english": "Grounding",
   "definition": "讓模型輸出盡量依循指定資料、工具結果或可查證來源，而非只依模型內部參數生成。",
   "why": "有依據仍要測試來源是否相關、完整、最新，以及回答是否忠實反映來源。",
   "related": "/knowledge/ai-agent/rag-evaluation-groundedness-test-set/"
  },
  {
   "slug": "fine-tuning",
   "category": "data-rag",
   "name": "微調",
   "english": "Fine-tuning",
   "definition": "以特定資料進一步調整既有模型參數，使行為、格式或任務表現更符合目標。",
   "why": "它不等於把文件變成可更新的知識庫，也不會自動解決權限與來源引用。",
   "related": "/knowledge/ai-agent/rag-vs-fine-tuning-comparison/"
  },
  {
   "slug": "evaluation",
   "category": "evaluation",
   "name": "AI 評測",
   "english": "AI Evaluation",
   "definition": "使用測試資料、指標、人工判斷或對抗情境，量測 AI 在指定用途與部署條件下的表現與風險。",
   "why": "通用榜單不能取代公司真實資料、使用者與錯誤後果的情境評測。",
   "related": "/ai-templates/rag-evaluation-sheet/"
  },
  {
   "slug": "test-set",
   "category": "evaluation",
   "name": "測試集",
   "english": "Test Set",
   "definition": "用於評估、且不應被拿來反覆調整答案的一組代表性案例與預期結果。",
   "why": "案例要涵蓋一般、邊界、拒答、權限與高影響錯誤，並保存版本。",
   "related": "/knowledge/ai-agent/rag-evaluation-groundedness-test-set/"
  },
  {
   "slug": "acceptance-criteria",
   "category": "evaluation",
   "name": "驗收條件",
   "english": "Acceptance Criteria",
   "definition": "在測試前定義的通過、限制與停止門檻，可包含品質、風險、延遲、成本與人工負荷。",
   "why": "先定門檻可避免看完結果後才移動標準，讓 go／no-go 決策可追溯。",
   "related": "/knowledge/enterprise-ai/ai-poc-to-production-checklist/"
  },
  {
   "slug": "hallucination",
   "category": "evaluation",
   "name": "幻覺／無依據生成",
   "english": "Hallucination",
   "definition": "模型產生看似合理、但與事實或指定來源不一致，或缺乏足夠依據的輸出。",
   "why": "不要只量整體正確率；需追蹤嚴重錯誤、來源忠實度、拒答與人工修正。",
   "related": "/knowledge/ai-agent/rag-evaluation-groundedness-test-set/"
  },
  {
   "slug": "precision-recall",
   "category": "evaluation",
   "name": "精確率與召回率",
   "english": "Precision and Recall",
   "definition": "精確率關心找出的結果有多少正確；召回率關心應找出的結果有多少被找出。",
   "why": "兩者常有取捨，門檻要依漏掉與誤報的實際成本決定。",
   "related": "/knowledge/enterprise-ai/ai-adoption-kpi-metrics/"
  },
  {
   "slug": "red-teaming",
   "category": "evaluation",
   "name": "AI 紅隊測試",
   "english": "AI Red Teaming",
   "definition": "以對抗與濫用角度測試模型、應用、基礎設施及運行行為，尋找可被利用或失控的路徑。",
   "why": "紅隊不能取代一般品質、隱私、法遵與營運測試，發現也需要修復與重測。",
   "related": "/knowledge/enterprise-ai/ai-incident-response-plan/"
  },
  {
   "slug": "drift",
   "category": "evaluation",
   "name": "漂移",
   "english": "Drift",
   "definition": "資料、使用方式、環境或模型行為隨時間改變，使原本評測與門檻不再代表現況。",
   "why": "需要持續監控、版本記錄與觸發重測，不能只在上線前驗一次。",
   "related": "/knowledge/enterprise-ai/ai-observability-logging-monitoring/"
  },
  {
   "slug": "human-in-the-loop",
   "category": "evaluation",
   "name": "人在迴路",
   "english": "Human in the Loop, HITL",
   "definition": "在 AI 流程中安排人員審查、批准、修正或接手，但實際權限與介入時機必須明確。",
   "why": "有人看過不等於有效控制；人員要有資訊、時間、能力與否決權。",
   "related": "/knowledge/enterprise-ai/ai-project-raci-roles/"
  },
  {
   "slug": "ai-governance",
   "category": "governance",
   "name": "AI 治理",
   "english": "AI Governance",
   "definition": "用政策、角色、流程、技術控制與證據，管理 AI 全生命週期的價值、責任與風險。",
   "why": "治理不是單次審查或一份原則宣告，而是持續盤點、量測、管理與改善。",
   "related": "/knowledge/enterprise-ai/enterprise-ai-adoption-roadmap/"
  },
  {
   "slug": "ai-inventory",
   "category": "governance",
   "name": "AI 系統清冊",
   "english": "AI System Inventory",
   "definition": "集中記錄 AI 用途、owner、模型、供應商、資料、使用者、風險、測試、監控與退場狀態。",
   "why": "沒有清冊就難以知道哪些系統需更新、通報、重測、停用或回應主管機關。",
   "related": "/knowledge/enterprise-ai/ai-system-inventory-guide/"
  },
  {
   "slug": "risk-tolerance",
   "category": "governance",
   "name": "風險容忍度",
   "english": "Risk Tolerance",
   "definition": "組織對特定情境可接受風險程度與必須升級、限制或停止的界線。",
   "why": "應由有權者依影響與資源決定，不能只讓開發團隊自行接受重大風險。",
   "related": "/tools/ai-risk-triage/"
  },
  {
   "slug": "inherent-residual-risk",
   "category": "governance",
   "name": "固有風險與剩餘風險",
   "english": "Inherent and Residual Risk",
   "definition": "固有風險先看未考慮控制時的情境風險；剩餘風險則是控制實施後仍存在的風險。",
   "why": "分開評估可避免因已有控制，就低估用途本身可能造成的嚴重影響。",
   "related": "/knowledge/enterprise-ai/taiwan-high-risk-ai-definition-and-preparation/"
  },
  {
   "slug": "high-risk-ai",
   "category": "governance",
   "name": "高風險 AI",
   "english": "High-risk AI",
   "definition": "依適用法律或主管框架，因特定用途與可能影響而需要更嚴格管理的 AI 應用；不是單靠模型名稱或本站分數判定。",
   "why": "正式認定、警語、責任與救濟要求須看主管機關及目的事業規範。",
   "related": "/taiwan-ai-governance/"
  },
  {
   "slug": "explainability-transparency",
   "category": "governance",
   "name": "可解釋性與透明",
   "english": "Explainability and Transparency",
   "definition": "可解釋性聚焦理解輸出或系統行為；透明則涵蓋 AI 角色、用途、資料、限制、責任與申訴資訊。",
   "why": "兩者需依受眾與影響提供適當資訊，不等於公開機密或個資。",
   "related": "/knowledge/enterprise-ai/ai-transparency-labeling-taiwan-guide/"
  },
  {
   "slug": "data-minimization",
   "category": "governance",
   "name": "資料最小化",
   "english": "Data Minimization",
   "definition": "只蒐集、使用與保存完成明確目的所必要的資料，並限制存取與期間。",
   "why": "模型能處理更多資料不代表應全部提供；提示、日誌、備份與供應商也要納入。",
   "related": "/knowledge/enterprise-ai/generative-ai-use-policy-checklist/"
  },
  {
   "slug": "shadow-ai",
   "category": "governance",
   "name": "Shadow AI",
   "english": "Shadow AI",
   "definition": "未經組織核准、盤點或治理而被員工、團隊或供應商使用的 AI 工具與流程。",
   "why": "完全禁止可能讓使用轉入地下；應先盤點需求、資料與替代方案，再分級處理。",
   "related": "/knowledge/enterprise-ai/shadow-ai-inventory-governance/"
  },
  {
   "slug": "guardrail",
   "category": "governance",
   "name": "護欄",
   "english": "Guardrail",
   "definition": "用來限制輸入、輸出、權限或動作的規則、模型、流程與人工控制總稱。",
   "why": "護欄可能被繞過或誤擋，必須測試、監控，且不能取代用途限制與責任。",
   "related": "/knowledge/automation/n8n-security-credentials-monitoring-checklist/"
  },
  {
   "slug": "ai-incident",
   "category": "governance",
   "name": "AI 事件與近失事件",
   "english": "AI Incident and Near Miss",
   "definition": "AI 造成或差點造成資料、權益、安全、服務、財產或信任影響的異常與失敗。",
   "why": "近失事件能在真正損害前暴露控制缺口，也應記錄、調查與改善。",
   "related": "/ai-templates/ai-incident-report/"
  }
 ]
}