Writing in the Workspace

Writing helps teams produce high-quality, on-brand text with the speed of AI and the assurance of expert human review. 
In a single workspace, you can review model outputs, refine responses, compare changes, and track quality, so content is accurate, consistent, and ready for production.

What is Writing?

Writing is a workflow for generating and validating short- and long-form text (e.g., product copy, support replies, descriptions). AI produces initial drafts; Sama experts refine and validate them against your instructions, style, and compliance needs. You get fast scale and reliable quality.

Understanding the Writing Workflow

Sama reviews and validates client data generated by AI models to ensure accuracy and quality across images, videos, and text. This includes evaluating model outputs (e.g., checking that images are free from unwanted text/logos, that AI-generated backgrounds are contextually relevant, and that prompts/responses follow predefined criteria).

Prompt

In the Workspace, every task centers around a prompt that can include an image, a video, and/or text.

The prompt is an object that may contain:

• Media (e.g., an image or video)
• A text instruction or question (e.g., text under the prompt)
• One or more responses generated by a model or an annotator

Below the media and question, you’ll see a response; this is where the annotation/answer lives.

 

 

 

Response Changes

% Change shows how much a response was modified versus a comparison point. It’s computed as: (additions + deletions) ÷ (additions + deletions + original word count).

Use the arrow icon to expand and view the exact diffs. If you are on Step B, you can compare the current answer with the Original (customer-provided) or the Latest (the most recent Step-A answer). Clicking Compare reveals what changed between the selected pair.

 

 

How to Review Writing Tasks in the Sampling Portal

In the Sampling Portal, you can review an annotated task, compare it against the original response, and leave feedback on specific responses. Learn more in the Sampling Portal guide here.

Quality Metrics

Internal quality metrics are computed by comparing QA and annotator answers. Reporting includes Answer Word Count, Additions, and Deletions, from which a quality metric can be inferred.

External (customer-facing) quality metrics are based on errors flagged by customers using highlights. We surface Answer Word Count, Highlight Count, and Highlight Word Count.

For all feedback contexts, the quality metric indicates the percentage of the response that needs to change:

This calculation is word-count-based and shows the extent of required modifications.

Writing Delivery Experience

This section explains the JSON format used to deliver Writing tasks. It covers the object model, parent/child relationships, and how to locate annotations.

Understanding the JSON Structure

Each task record contains an answers.objects array. Every entry is an object with:

• id : unique identifier for the object
• parent_id : the parent object’s id (if any)
• sort_index : initial UI order (optional)
• class_name : object type (e.g., prompt, response, text, image, video, metadata, skip)
• tags : content/attributes (e.g., text for annotations, url for media)

Parent → Child Pattern

prompt (id: X)
├── image (parent_id: X)
├── text  [instruction/question] (parent_id: X)
└── response (id: Y, parent_id: X)
    └── text  [annotation/answer] (parent_id: Y)

In practice, this means: the annotation you care about is usually a text object whose parent_id is the id of a response object.

Minimal Example

[
   {
      "id":"task-001",
      "answers":{
         "objects":[
            {
               "id":1,
               "class_name":"prompt"
            },
            {
               "id":2,
               "parent_id":1,
               "class_name":"response"
            },
            {
               "id":9,
               "parent_id":1,
               "class_name":"image",
               "tags":{
                  "url":"https://example.com/dog.png"
               }
            },
            {
               "id":10,
               "parent_id":1,
               "class_name":"text",
               "tags":{
                  "text":"Describe what you see in the image."
               }
            },
            {
               "id":11,
               "parent_id":2,
               "class_name":"text",
               "tags":{
                  "text":"This is a happy dog."
               }
            }
         ]
      }
   }
]

Where is the annotation? It’s the text object with parent_id: 2 (the response’s id). Its value is in tags.text: “This is a happy dog.”

Richer Example 

The same logic holds even when tasks include additional information (e.g., metadata, multiple images). Below is a trimmed, realistic example showing how to find the annotation:

[
   {
      "id":"123",
      "project_id":78965,
      "state":"completed",
      "answers":{
         "objects":[
            {
               "id":1,
               "sort_index":1,
               "class_name":"prompt",
               "tags":{
                  
               }
            },
            {
               "id":2,
               "parent_id":1,
               "sort_index":0,
               "class_name":"response",
               "tags":{
                  
               }
            },
            {
               "id":9,
               "parent_id":1,
               "sort_index":2,
               "class_name":"image",
               "tags":{
                  "url":"https://...",
                  "assetId":"333"
               }
            },
            {
               "id":10,
               "parent_id":1,
               "sort_index":1,
               "class_name":"text",
               "tags":{
                  "text":"Given the following two dog images, describe the image."
               }
            },
            {
               "id":11,
               "parent_id":2,
               "sort_index":1,
               "class_name":"text",
               "tags":{
                  "text":"This is a happy dog."
               }
            },
            {
               "id":12,
               "parent_id":1,
               "sort_index":3,
               "class_name":"image",
               "tags":{
                  "url":"https://...",
                  "assetId":"1111"
               }
            },
            {
               "id":18,
               "sort_index":1,
               "class_name":"metadata",
               "tags":{
                  "before_datetime":"2021-11-09 16:52:31",
                  "after_datetime":"2021-11-13 16:36:55",
                  "texts_assistant":"Looking at this small dog, what can you describe about the hair?"
               }
            },
            {
               "id":20,
               "sort_index":1,
               "class_name":"skip",
               "tags":{
                  "skip_task":"No"
               }
            }
         ]
      }
   }
]

Where is the annotation? Find the response (id: 2), then find its children. The child text with parent_id: 2 holds the annotation: “This is a happy dog.”

How to Locate Annotations

1. Find the prompt object (often the first object for a task).
2. Find the response whose parent_id equals the prompt’s id.
3. Find the text object(s) whose parent_id equals that response’s id.
4. Read tags.text  That string is the annotation/answer (e.g., “This is a happy dog.” or “In the image you can see a sunny day”).
    

💡To find the final written description, look for a text object whose parent_id equals the id of a response object.

Quick Reference

Fields Reference

• id: unique identifier for an object
• parent_id: id of the parent object (optional)
• sort_index: initial ordering in the UI (optional)
• tags: static attributes/content of the object (e.g., text, url, metadata)
• class_name: object type (e.g., prompt, response, text, image, video, metadata, skip)