本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

搭配 Amazon Q in Connect 使用生成式 AI 技術的自助服務

Amazon Q in Connect 支援聊天和語音 (IVR) 管道中的客戶自助式使用案例。它可以：

當客戶需要額外協助時，Amazon Q in Connect 會順暢地將客戶轉接給客服人員，同時保留完整對話的內容。

預設系統工具

Amazon Q in Connect 隨附下列內建工具，可out-of-the-box：

您可以自訂這些預設工具，以符合您的特定需求。

設定自助式服務

請依照下列步驟啟用 Amazon Q in Connect for self-service：

自助服務的自訂動作

您可以新增自訂工具來擴展 Amazon Q in Connect 的功能。這些工具可以：

將自訂工具新增至 AI 提示時：

範例：淡化客戶意圖

您可以建立生成式 AI 助理，在路由至代理程式之前收集資訊。這需要：

以下是解構的範例工具定義。您可以移除 CONVERSATION 以外的所有預設工具，並新增名為 HANDOFF 的新自訂工具：

tools:
- name: CONVERSATION
  description: Continue holding a casual conversation with the customer.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The message you want to send next to hold a conversation and get an understanding of why the customer is calling.
    required:
    - message
- name: HANDOFF
  description: Used to hand off the customer engagement to a human agent with a summary of what the customer is calling about.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: Restatement to the customer of what you believe they are calling about and any pertinent information. MUST end with a statement that you are handing them off to an agent. Be as concise as possible.
      summary:
        type: string
        description: A list of reasons the customer has reached out in the format <SummaryItems><Item>Item one</Item><Item>Item two</Item></SummaryItems>. Each item in the Summary should be as discrete as possible.
    required:
    - message
    - summary

範例：建議客戶採取動作

您可以使用流程 Amazon Connect 在 中設定下一個最佳動作。您也可以設定自動化動作並建立step-by-step指南，為客戶提供以 UI 為基礎的動作。如需詳細資訊，請參閱設定 Amazon Connect 客服人員工作區Step-by-step指南。  Amazon Q in Connect 會將選取的工具名稱儲存為 Lex 工作階段屬性。然後，您可以使用檢查聯絡屬性流程區塊來存取 屬性。 

以下是預訂行程的範例工具定義：

name: TRIP_BOOKING
  description: Tool to transfer to another bot who can do trip bookings. Use this tool only when the last message from the customer indicates they want to book a trip or hotel.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The polite message you want to send while transferring to the agent who can help with booking.
    required:
    - message

使用檢查聯絡屬性流程區塊來判斷 Amazon Q in Connect 已選取哪些工具時，您可以做出分支決策，為該使用者選取相關的step-by-step指南。例如，如果客戶想要在自助式聊天互動期間預訂行程，您可以：

如需在聊天中實作step-by-step指南的詳細資訊，請參閱 在 Amazon Connect 聊天中部署step-by-step指南。

FOLLOW_UP_QUESTION 工具

FOLLOW_UP_QUESTION 工具透過與客戶進行更具互動和資訊收集的對話，增強了 Amazon Q in Connect 自助服務功能。此工具可與預設和自訂工具搭配使用。它有助於在決定要採取的動作之前收集必要的資訊。

下列程式碼顯示 FOLLOW_UP_QUESTION 工具的組態。

- name: FOLLOW_UP_QUESTION
  description: Ask follow-up questions to understand customer needs, clarify intent, 
    and collect additional information throughout the conversation. Use this to gather 
    required details before selecting appropriate actions.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The message you want to send next in the conversation with the 
          customer. This message should be grounded in the conversation, polite, and 
          focused on gathering specific information.
    required:
      - message

FOLLOW_UP_QUESTION 工具透過讓 Amazon Q in Connect 在決定要採取的動作之前收集必要資訊，來補充您定義的工具。它特別適用於：

範例 FOLLOW_UP_QUESTION 使用案例

對於旨在報告詐騙的自助式機器人，您可以定義名為 CONFIRM_SUBMISSION 的工具，以收集客戶的特定資訊：

- name: CONFIRM_SUBMISSION
  description: Confirm all collected information and finalize the report submission.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: A message reviewing all of the collected information and asking 
          for final confirmation before submission.
      report_details:
        type: string
        description: The user's report or complaint details
      reporter_info:
        type: string
        description: Reporter's contact information (if provided) or "Anonymous"
      subject_info:
        type: string
        description: Information about the individual or business being reported
    required:
      - message
      - report_details
      - reporter_info
      - subject_info

不過，您可以改為使用 FOLLOW_UP_QUESTION step-by-step收集此資訊，如下列範例所示：

- name: FOLLOW_UP_QUESTION
  description: Ask follow-up questions to understand customer needs and collect additional 
    information throughout the complaint process. Use this for all information gathering 
    steps including confidentiality preferences, contact info, subject details etc.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The message you want to send next in the conversation with the 
          customer. This message should be grounded in the conversation and polite. 
          Use this for asking clarification questions, collecting contact information, 
          gathering subject details, and all other follow-up steps in the complaint 
          process.
    required:
      - message

提示說明

將指示新增至您的提示，引導您的自助式機器人了解何時使用 FOLLOW_UP_QUESTION 工具。例如：

CRITICAL: Use FOLLOW_UP_QUESTION for all information gathering steps after the initial analysis. 
Do NOT proceed to other tools until you have collected all required information. Use this tool 
to disambiguate customer intent when unclear.

When using FOLLOW_UP_QUESTION:
1. Ask one specific question at a time
2. Focus on collecting required information for the most likely intent
3. Be conversational but direct
4. Acknowledge information the customer has already provided
5. Only move to other tools (like CONFIRM_SUBMISSION or TRIP_BOOKING) when you have all required 
   information

對話範例

以下是顯示使用中 FOLLOW_UP_QUESTION 工具的三個對話範例。

<examples>
    <example>
        <conversation>
            [CUSTOMER] I want to report someone who I think is committing fraud
        </conversation>
        <thinking>
            The customer wants to report fraud. I need to collect specific information 
            including details about the suspected fraud, reporter information, and 
            subject information. I'll start by asking about the suspected fraudulent activity.
        </thinking>
        <tool>
            [FOLLOW_UP_QUESTION(message="I'd be happy to help you report suspected fraud. 
            Could you please describe the fraudulent activity you've observed?")]
        </tool>
    </example>
    
    <example>
        <conversation>
            [AGENT] Can you provide me your full address?
            [CUSTOMER] I don't feel comfortable sharing my address
        </conversation>
        <thinking>
            The caller declined to share their address. I should respect their privacy 
            concerns while still trying to collect alternative contact information.
        </thinking>
        <tool>
            [FOLLOW_UP_QUESTION(message="I understand your privacy concerns. That's 
            completely fine. Would you be comfortable providing a phone number or email 
            address instead? Or you can choose to remain anonymous.")]
        </tool>
    </example>
    
    <example>
        <conversation>
            [CUSTOMER] I'm not getting my benefit payments anymore
        </conversation>
        <thinking>
            The customer is reporting a payment issue. This sounds like a general service 
            issue rather than fraud. I should clarify if they need assistance with their benefits.
        </thinking>
        <tool>
            [FOLLOW_UP_QUESTION(message="I'm sorry to hear about the issue with your payments. 
            To help me understand better, could you tell me when you last received a payment 
            and if you've received any notification about why they stopped?")]
        </tool>
    </example>
</examples>