{
  "datasetId": "ai-product-manager-technical-glossary-v1",
  "schemaVersion": "1.0.0",
  "createdAt": "2026-07-21",
  "updatedAt": "2026-07-21",
  "language": "zh-CN",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "disclosureZh": "DragonAI 运营本网站与 AI 产品经理课程。本数据集是面向产品决策的第一方术语整理，不是行业标准、平台认证或效果承诺。",
  "terms": [
    {
      "termId": "rag",
      "nameZh": "检索增强生成",
      "abbreviation": "RAG",
      "definitionZh": "在生成前从外部语料检索候选材料，并把相关上下文提供给生成模型。",
      "productQuestionZh": "答案是否依赖可更新、可授权或需要保留来源的外部知识？",
      "acceptanceEvidenceZh": [
        "固定查询的检索命中",
        "上下文支持答案",
        "权限过滤",
        "无答案处理",
        "延迟与成本"
      ],
      "doesNotProveZh": "接入知识库后答案一定正确。",
      "evidenceIds": [
        "rag-original-paper-neurips-2020",
        "microsoft-foundry-rag-indexes-20260720"
      ]
    },
    {
      "termId": "embedding",
      "nameZh": "向量表示",
      "abbreviation": "Embedding",
      "definitionZh": "把文本或其他对象映射为可比较的数值向量，用于召回、聚类或匹配候选。",
      "productQuestionZh": "目标查询能否在自己的语料、语言和领域术语中召回相关候选？",
      "acceptanceEvidenceZh": [
        "目标语料召回率",
        "关键切片表现",
        "过滤组合",
        "版本漂移"
      ],
      "doesNotProveZh": "向量距离等于业务相关性。",
      "evidenceIds": [
        "microsoft-foundry-rag-indexes-20260720"
      ]
    },
    {
      "termId": "reranker",
      "nameZh": "重排序器",
      "abbreviation": "Reranker",
      "definitionZh": "对初步召回候选按查询相关性进行第二阶段排序。",
      "productQuestionZh": "正确材料已经进入候选集，但前几条排序是否不稳定？",
      "acceptanceEvidenceZh": [
        "top-k 命中变化",
        "最终答案变化",
        "P95 延迟",
        "单位成本"
      ],
      "doesNotProveZh": "能恢复初检阶段完全漏掉的材料。",
      "evidenceIds": [
        "microsoft-advanced-rag-systems-20260720"
      ]
    },
    {
      "termId": "fine_tuning",
      "nameZh": "模型微调",
      "abbreviation": "Fine-tuning",
      "definitionZh": "用训练样本调整模型参数，使其行为更适合特定任务或输出模式。",
      "productQuestionZh": "问题是否需要稳定改变任务行为，而不是提供频繁变化的外部事实？",
      "acceptanceEvidenceZh": [
        "锁定测试集表现",
        "泛化",
        "退化检查",
        "训练与维护成本"
      ],
      "doesNotProveZh": "适合充当频繁更新且可追溯的事实数据库。",
      "evidenceIds": [
        "microsoft-rag-fine-tuning-choice-20260720",
        "aws-rag-fine-tuning-comparison-20260720"
      ]
    },
    {
      "termId": "agent",
      "nameZh": "智能体",
      "abbreviation": "Agent",
      "definitionZh": "把模型、工具、指令和控制逻辑放入有状态循环，由系统根据中间结果推进多步任务。",
      "productQuestionZh": "任务是否需要动态选择动作、使用工具并根据结果继续决策？",
      "acceptanceEvidenceZh": [
        "逐步轨迹",
        "任务成功率",
        "工具成功率",
        "停止条件",
        "人工接管与恢复"
      ],
      "doesNotProveZh": "使用大模型就自动成为可靠智能体。",
      "evidenceIds": [
        "openai-practical-guide-building-agents-20260721"
      ]
    },
    {
      "termId": "tool_calling",
      "nameZh": "工具调用",
      "abbreviation": "Tool calling",
      "definitionZh": "模型按结构选择工具和参数，应用在校验与授权后执行外部函数或 API。",
      "productQuestionZh": "模型是否需要读取或改变外部系统状态？",
      "acceptanceEvidenceZh": [
        "参数校验",
        "最小权限",
        "幂等",
        "错误处理",
        "成功与失败审计"
      ],
      "doesNotProveZh": "模型生成参数就已完成被授权且正确的业务动作。",
      "evidenceIds": [
        "openai-practical-guide-building-agents-20260721"
      ]
    },
    {
      "termId": "mcp",
      "nameZh": "模型上下文协议",
      "abbreviation": "MCP",
      "definitionZh": "采用主机、客户端与服务器架构，通过能力协商连接 AI 应用与外部资源、工具和提示等能力。",
      "productQuestionZh": "多个 AI 应用或外部能力是否需要一致、可协商的连接层？",
      "acceptanceEvidenceZh": [
        "能力协商",
        "连接边界",
        "授权",
        "超时与审计",
        "版本兼容"
      ],
      "doesNotProveZh": "自动提供任务规划、结果正确性或完整安全模型。",
      "evidenceIds": [
        "mcp-architecture-spec-20250618"
      ]
    },
    {
      "termId": "evaluation_set",
      "nameZh": "评测集",
      "abbreviation": "Evaluation set",
      "definitionZh": "由固定任务、期望或评分规则、失败切片、指标和阈值组成的版本化比较基准。",
      "productQuestionZh": "不同方案是否能在相同任务与口径下复算和比较？",
      "acceptanceEvidenceZh": [
        "真实任务覆盖",
        "失败切片",
        "逐条输出",
        "指标方向",
        "发布阈值",
        "版本"
      ],
      "doesNotProveZh": "离线通过等于线上用户结果必然提升。",
      "evidenceIds": [
        "google-cloud-genai-evaluation-metrics-20260721"
      ]
    }
  ],
  "roleSeparationZh": {
    "rag": "为回答检索和提供相关外部材料",
    "agent": "根据目标与中间结果决定下一步动作",
    "mcp": "用主机、客户端、服务器与能力协商连接资源和工具"
  },
  "compositionPatterns": [
    {
      "patternId": "approved_knowledge_answer",
      "userTaskZh": "从已批准文档回答制度问题",
      "minimumArchitectureZh": "检索 + 生成，先做 RAG 基线",
      "primaryUncertaintyZh": "能否找到并依据正确材料回答",
      "avoidZh": "不要因为流程有两步就先引入自主 Agent"
    },
    {
      "patternId": "state_change_workflow",
      "userTaskZh": "查询订单后发起退款",
      "minimumArchitectureZh": "工具调用 + 明确工作流；高风险步骤人工确认",
      "primaryUncertaintyZh": "身份、权限、参数、幂等和外部执行结果",
      "avoidZh": "不要把模型生成的参数直接当作已授权动作"
    },
    {
      "patternId": "dynamic_investigation",
      "userTaskZh": "在多个系统调查故障并形成处置建议",
      "minimumArchitectureZh": "受限 Agent + 工具；连接层可采用 MCP",
      "primaryUncertaintyZh": "能否根据中间结果选择正确数据源和动作",
      "avoidZh": "不要开放未登记工具或省略预算、停止与接管条件"
    },
    {
      "patternId": "retrieve_then_act",
      "userTaskZh": "先查知识库，再按结果调用业务系统",
      "minimumArchitectureZh": "RAG 负责检索，Agent 负责编排，MCP 可统一连接",
      "primaryUncertaintyZh": "知识、控制和接口三层能否分别通过并端到端协作",
      "avoidZh": "不要把 MCP 服务器存在误报为 Agent 已可靠完成任务"
    }
  ],
  "decisionOrderZh": [
    "定义用户任务与失败代价",
    "判断缺口属于知识、行为、行动还是评测",
    "从最小基线逐步增加组件",
    "用同一评测集比较质量、风险、延迟、成本和维护",
    "把权限、接管、监测与回滚写入发布门槛"
  ],
  "sourceArticle": "https://course.dragonai.tech/guides/ai-product-manager-technical-glossary",
  "title": "AI 产品经理 8 个核心技术术语决策地图 v1",
  "description": "把 RAG、Embedding、Reranker、微调、Agent、工具调用、MCP 与评测集拆成定义、产品问题、验收证据和不可推论边界。",
  "url": "https://course.dragonai.tech/datasets/ai-product-manager-technical-glossary-v1.json",
  "creator": "烛龙智元内容研究组"
}