> For the complete documentation index, see [llms.txt](https://docs.maiagent.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.maiagent.ai/maiagent-user-guide/maiagent-user-guide-en/application/text/productsearch.md).

# Product Inquiry Assistant

In enterprise product customer service, handling a large volume of frequent customer inquiries through manual responses not only leads to delays but also places excessive burden on customer service staff, affecting overall service efficiency. Many repetitive questions appear over and over again, consuming significant time to handle, making it difficult for teams to focus their energy on more complex customer needs and problem resolution. Additionally, the lack of personalized recommendations based on customer behavior and needs fails to effectively improve customer satisfaction and user experience, further impacting brand image and business growth.

Now, you can leverage MaiAgent to build an AI product query assistant for external-facing enterprise use, which not only effectively reduces customer service costs but also provides personalized product recommendations closer to customer needs, improving service quality and customer satisfaction!

## Use Case: Build an External Product Query Assistant for a Computer Brand

Suppose you are a product customer service specialist for a computer brand, facing a large volume of diverse specification inquiries from customers that require significant time to handle and respond to. In this case, you can leverage MaiAgent to build an AI product query customer service assistant for external use, improving work efficiency.

You can leverage MaiAgent to build an AI product query customer service assistant for external use, providing instant responses that not only dramatically improve work efficiency but also effectively reduce customer service burden, allowing you to focus on higher-value customer service work.

## Workflow

### 1. Create an External AI Product Query Assistant

Enter the AI assistant name.

<figure><img src="/files/WySHbDPydBnH4ThijUAM" alt=""><figcaption></figcaption></figure>

Select RAG and language model.

<figure><img src="/files/ZcWUcieerWz199EYRuks" alt=""><figcaption></figcaption></figure>

When selecting the response mode, choose "Standard Mode" if you have no special requirements, which is suitable for this use case. Refer to the following role instructions:

```
# Role
You are the company's external product query assistant.

# Output Format
Use the example format below to reply with the three most relevant knowledge items.

<example>
Based on your question, we recommend the following products:

1. [Product Name] Specification details
2. [Product Name] Specification details
3. [Product Name] Specification details
</example>

# Output Restrictions
- Reply in Traditional Chinese
- Prioritize more recent data as reference material
- Do not answer questions outside the knowledge base scope
- Answer based on knowledge base data; if unable to answer, use the text within the <example> below

<example>
Sorry, this question is beyond the scope of our responses. Please contact a human customer service representative for assistance.
```

<figure><img src="/files/xJ0w6PV3HdgxwPEXBQBX" alt=""><figcaption></figcaption></figure>

### 2. Upload Knowledge Base

Here we use HP's publicly available product catalog as a sample upload.

<https://www.hptw-ebrochure.com/hipershop/rwd1185/store/F2/2024-0824_compressed.pdf>

<figure><img src="/files/to7MWxFAziDjClStGDqp" alt=""><figcaption></figcaption></figure>

### 3. Deploy the AI Assistant

Now you can embed the AI assistant directly into your company website, allowing customers to ask questions instantly through the chat interface, providing personalized product recommendation options closer to their needs, and improving user experience and satisfaction!

<figure><img src="/files/Z3ifXEWTOaRa7oG3nLSQ" alt=""><figcaption></figcaption></figure>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.maiagent.ai/maiagent-user-guide/maiagent-user-guide-en/application/text/productsearch.md?ask=<question>&goal=<endgoal>
```

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.