# Response Fields

## **Top-Level Fields**

<table data-full-width="false"><thead><tr><th width="185.6666259765625">Field</th><th width="306">Description</th><th>Example</th></tr></thead><tbody><tr><td><strong><code>traceID</code></strong></td><td>Unique identifier for the request. Used for debugging, support, and internal tracking.</td><td><p><code>"traceID": "71f9e0b2-3a77-4ad0-</code></p><p><code>9a61-23b7e1bfa8d1"</code></p></td></tr><tr><td><strong><code>timestamp</code></strong></td><td>ISO-8601 timestamp indicating when the request was processed.</td><td><p><code>"timestamp": "2025-12-08</code></p><p><code>T11:39:19.269Z"</code></p></td></tr><tr><td><strong><code>request_duration</code></strong></td><td>End-to-end request duration in seconds (from request received to response returned).</td><td><code>"request_duration": 6.3</code></td></tr><tr><td><strong><code>process_duration</code></strong></td><td>Total internal processing time in seconds. This includes the full request lifecycle.</td><td><code>"process_duration": 6.4</code></td></tr></tbody></table>

**Note**\
`request_duration` is included within `process_duration`.\
For example, if `request_duration` is `6.3` and `process_duration` is `6.4`, the overall time experienced by the client is **6.4 seconds**.

***

## **Response Object**

The main parsed output from the LLM.

***

### `prompt`

The original prompt sent to the LLM for this request.\
This field is always returned as part of the response for traceability and debugging.

**Example**

```json
"prompt": "what is the best seo tips in 2025"
```

***

### **`text` (plain text)**

Plain-text version of the response with formatting removed.\
Useful for storage, indexing, or systems that don’t support Markdown.

**Example**

```json
"text": "Here are the leading SEO tips for 2025 include focusing on content quality and user experience."
```

***

### **`citations_found`**

Boolean indicating whether the model output included citations.

**Example**

```json
"citations_found": true
```

***

### **`citations`**

Flat list of citation objects extracted from the Google AI Mode UI.

Each citation includes:

* `id`
* `title`
* `url`
* `section`: `"citations"` or `"more"`

**Example**

```json
"citations": [
  {
    "id": 1,
    "title": "Best Headphones of 2025",
    "url": "https://example.com/best-headphones-2025",
    "section": "citations"
  }
]
```

***

### `inline_citations`

Structured citations extracted directly from the main response text.

Each inline citation represents a **specific source tied to an exact text span (anchor)** in the generated answer.

Unlike `citations`, which are a flat list of sources, `inline_citations` show **where and how each source is used inside the content**.

Each inline citation includes:

* `id`
* `title`
* `url`
* `text_anchor`

**Example**

```json
"inline_citations": [
  {
    "id": 1,
    "url": "https://example.com/ai-productivity-report",
    "title": "AI Productivity Trends 2025",
    "text_anchor": "AI tools have increased developer productivity by up to 40% in enterprise environments."
  },
  {
    "id": 2,
    "url": "https://example.com/startup-ai-adoption",
    "title": "Startup Adoption of AI Tools",
    "text_anchor": "Startups are adopting AI solutions faster than enterprises due to fewer legacy constraints."
  },
  {
    "id": 3,
    "url": "https://example.com/ai-productivity-report",
    "title": "AI Productivity Trends 2025",
    "text_anchor": "AI tools have increased developer productivity by up to 40% in enterprise environments."
  }
]
```

Notes

* A single text span may have **multiple inline citations**
* `id` corresponds to the same source in the `citations` array


---

# 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/response-fields.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.