# Example Requests

In this example, we send a simple LLM Scraper request using **Google AI Mode**, asking for leading SEO tips for 2026.\
\
Google AI Mode performs AI-powered web retrieval and synthesis, returning a consolidated answer backed by cited sources.

#### Request

```bash
curl -X POST https://llm-scraper.netnut.io/search \
  -U "username:password" \
  -H "Content-Type: application/json" \
  -d '{
    "engine": "google-ai-mode",
    "prompt": "what are the leading SEO tips in 2026"
  }'
```

***

#### Example Response

Example shape – your actual `traceID`, timestamps, and content will vary.

```json
{
  "traceID": "aa11234-b2345-10a2-a12c-df3d14368c64",
  "timestamp": "2026-01-28T16:51:22.809Z",
  "request_duration": 9.09,
  "process_duration": 9.09,
  "scraper": "Google-AI-Mode",
  "response": {
    "prompt": "what are the leading SEO tips in 2025",

    "text": "The leading SEO tips for 2025 focus on creating high-quality, user-first content that demonstrates expertise and aligns with AI-powered search, while ensuring an excellent technical and user experience (UX)...",

    "citations_found": true,
    "citations": [
      {
        "id": 1,
        "title": "Top SEO Strategies for 2025 to Drive Real Growth",
        "url": "https://example.com/seo-strategies-2025",
        "section": "citations"
      },
      {
        "id": 2,
        "title": "2025 SEO Trends: What Matters Most",
        "url": "https://example.com/seo-trends-2025",
        "section": "citations"
      }
    ]
  }
}
```

***

#### Response Field Explanation

In this response:

* **model / engine**\
  Google AI Mode is used to generate the response, leveraging AI-driven web retrieval and synthesis.
* **prompt**\
  The original prompt submitted to the model.
* **text**\
  The full plain-text answer generated by Google AI Mode, synthesizing information from multiple authoritative sources.
* **citations\_found**\
  Indicates whether the model identified and returned source references.
* **citations**\
  A flat list of source objects used to support the response.\
  Each citation includes:
  * `id`: Sequential identifier
  * `title`: Source title
  * `url`: Source URL
  * `section`: Always `"citations"` for Google AI Mode


---

# Agent Instructions: 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:

```
GET https://help.netnut.io/netnut-documentation/netnut-scraper-apis/llm-scraper/google-ai-mode/example-requests.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

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.