# Response Fields

### **Top-Level Fields**

| Field                  | Description                                                                            | Example                                             |
| ---------------------- | -------------------------------------------------------------------------------------- | --------------------------------------------------- |
| **`traceID`**          | Unique identifier for the request. Used for debugging, support, and internal tracking. | `"traceID": "a1b2c3d4-e5f6-7a8b-9c0d-e1f2a3b4c5d6"` |
| **`timestamp`**        | ISO-8601 timestamp indicating when the request was processed.                          | `"timestamp": "2026-04-16T10:56:56.252Z"`           |
| **`request_duration`** | End-to-end request duration in seconds (from request received to response returned).   | `"request_duration": 14.28`                         |
| **`process_duration`** | Total internal processing time in seconds. This includes the full request lifecycle.   | `"process_duration": 14.28`                         |
| **`scraper`**          | Scraper name by LLM Engine                                                             | `"scraper": "Copilot"`                              |

**Note**\
`request_duration` is included within `process_duration`.\
For example, if `request_duration` is `14.28` and `process_duration` is `14.28`, the overall time experienced by the client is **14.28 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 are the leading SEO tips in 2026"
```

***

#### `text_markdown`

Markdown-formatted version of the response, preserving structure such as headings, lists, emphasis, and inline citations.

This field represents the **primary readable output** of the LLM, including formatting and citation markers as returned by the model.

**Example**

```json
"text_markdown": "**The leading SEO tips for 2026 center on AI-readiness, entity-based optimization, technical excellence, and delivering deeply useful, intent-matched content.**\n\n### 1. Optimize for AI-Driven Search & LLM Discovery\n- Build **topic clusters** and **entity relationships** instead of isolated keyword pages.\n- Use **structured data** and schema to make content easy for AI to cite."
```

***

#### **`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": "The leading SEO tips for 2026 center on AI-readiness, entity-based optimization, technical excellence, and delivering deeply useful, intent-matched content."
```

***

#### **`citations_found`**

Boolean indicating whether the model output included citations.

**Example**

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

***

#### **`citations`**

Flat list of citation objects extracted from Copilot.

Each citation includes:

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

**Example**

```json
"citations": [
  {
    "id": 1,
    "url": "https://moz.com/blog/top-seo-tips-2026-whiteboard-friday",
    "title": "Top SEO Tips For 2026 - Moz",
    "section": "citations"
  },
  {
    "id": 2,
    "url": "https://sitebulb.com/resources/guides/seo-in-2026-17-expert-tips-predictions/",
    "title": "SEO in 2026: 17 Expert Tips & Predictions - Sitebulb",
    "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://moz.com/blog/top-seo-tips-2026-whiteboard-friday",
    "title": "Top SEO Tips For 2026 - Moz",
    "text_anchor": "Build topic clusters and entity relationships instead of isolated keyword pages. Use structured data and schema to make content easy for AI to cite."
  },
  {
    "id": 2,
    "url": "https://sitebulb.com/resources/guides/seo-in-2026-17-expert-tips-predictions/",
    "title": "SEO in 2026: 17 Expert Tips & Predictions - Sitebulb",
    "text_anchor": "Improve crawlability, internal linking, and site architecture. Demonstrate E-E-A-T to become a trusted source for both humans and AI."
  }
]
```

**Notes**

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

***

#### `tables`

Structured tables extracted from the Copilot response.

Each table includes a `title`, `table_columns` array, and `table_rows` array where each cell is represented as an object with a `value` key.

**Example**

```json
"tables": [
  {
    "title": "Quick Comparison: What Matters Most in 2026",
    "table_columns": ["Priority", "Why It Matters"],
    "table_rows": [
      [{"value": "AI & Entity Optimization"}, {"value": "AI search relies on semantic understanding"}],
      [{"value": "Technical SEO"}, {"value": "Crawlability + speed directly impact rankings"}]
    ]
  }
]
```


---

# 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/copilot/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.