# 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><tr><td><strong><code>model</code></strong></td><td>LLM model used to generate the response.</td><td><code>"model": "gpt-5.2"</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_markdown`**

Markdown-formatted version of the answer, preserving headings, lists, emphasis, and links.

**Example**

```json
"text_markdown": "Here are the **leading SEO tips for 2025**:\n\n### 1. High-quality content\n### 2. Strong UX"
```

***

### **`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."
```

***

### **`model_search_queries`**

Search queries generated by the model during Web Search mode.\
These reflect how the model expanded or refined the original prompt.

**Example**

```json
"model_search_queries": [
  "leading SEO tips in 2025",
  "top SEO best practices 2025"
]
```

***

### **`widgets`**

Structured UI components extracted from the model output, such as product carousels or cards.

**Example**

```json
"widgets": [
  {
    "type": "product_carousel",
    "items": [
      {
        "type": "product_card",
        "name": "Headphones A",
        "price": 399.99,
        "currency": "$",
        "Images": ["https://.."],
        "merchants": [
          {
            "merchant": "Merchant A",
            "price": 399.99,
            "currency": "$",
            "url": "https://merchant-a.example/product/headphones-a"
          },
          {
            "merchant": "Merchant B",
            "price": 409.99,
            "currency": "$",
            "url": "https://merchant-b.example/product/headphones-a"
          }
        ]
      }
    ]
  }
]

```

***

### **`citations_found`**

Boolean indicating whether the model output included citations.

**Example**

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

***

### **`citations`**

Flat list of citation objects extracted from the ChatGPT 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"
  }
]
```

***

### `text_html`&#x20;

Raw HTML representation of the model’s rendered answer, as produced by the ChatGPT UI.

This field contains the original HTML markup of the response content (including layout structure, classes, and formatting).

**Example**

```json
"text_html": "<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-1\" data-message-author-role=\"assistant\" data-message-id=\"3b9247fc-daf4-480c-8a8b-69aa11bb8393\" data-message-model-slug=\"gpt-5-2\" dir=\"auto\">\n <div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[1px]\">\n  <div class=\"markdown prose dark:prose-invert w-full wrap-break-word light..."
```

***

### `raw_response` (Optional)

Raw streamed output from the underlying LLM session.\
Returned **only when `raw_response: true` is set in the request**.

Includes metadata events, delta messages, and tool outputs.

**Example**

```json
"raw_response": [
  "data: {\"type\":\"server_metadata\",\"metadata\":{\"model_slug\":\"gpt-5.2\"}}",
  "event: delta",
  "data: {...}"
]
```

**Notes**

* Disabled by default
* Returned **in addition to** parsed fields
* Intended for advanced use cases


---

# 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/chatgpt/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.