> ## Documentation Index
> Fetch the complete documentation index at: https://docs.latitude.so/llms.txt
> Use this file to discover all available pages before exploring further.

# Edit row contents

> Update the cells of an existing dataset row — over the API, the SDKs, or an MCP agent — to fill in expected output or correct any field after a row was created.

<Info>
  **Where this fits:** Part of **Refine**. Editing rows lets you curate a [dataset](./overview) after it is built — most commonly to add [expected output](./expected-output) to rows imported [from traces](./add-traces).
</Info>

Every [dataset](./overview) row is made of cells: the four built-in fields — **Input**, **Output**, **Expected output**, and **Metadata** — plus any [custom columns](./custom-columns) you have added. You can edit those cells by hand in the UI, or programmatically over the API, the SDKs, or an [MCP](../getting-started/mcp) agent.

## Edit a row in the UI

Open a dataset and open any row to edit its cells inline. Built-in fields and active custom columns each show their own editable field; the values you enter are saved with the row. See [Add expected output](./expected-output) for the most common case.

## Edit a row programmatically

Updates are **partial**: you send only the cells you want to change, and every cell you omit keeps its current value. This makes it safe to, for example, set `expectedOutput` on a row without resending its input or output.

How cells are addressed:

* **Built-in cells** use their field name: `input`, `output`, `expectedOutput`, `metadata`.
* **Custom columns** are set through a `custom` map keyed by the column's **stable identifier** (not its display name). Custom values are merged onto the row's existing ones, so columns you omit are left untouched. Unknown or [removed](./custom-columns) columns are rejected.

A successful edit creates a new dataset version and returns its id.

<Note>
  You need the row's **id** and, for custom columns, the column **identifiers**. List rows to get their ids, and list the dataset's columns to get identifiers — both built-in (`input`, `output`, …) and custom. These are available in the API, every SDK, and over MCP.
</Note>

### HTTP API

```
PATCH /v1/projects/{projectSlug}/datasets/{datasetSlug}/rows/{rowId}
```

| Field            | Type     | Required | Description                                                                   |
| ---------------- | -------- | -------- | ----------------------------------------------------------------------------- |
| `input`          | any JSON | No       | New input cell. Omit to leave unchanged.                                      |
| `output`         | any JSON | No       | New output cell. Omit to leave unchanged.                                     |
| `expectedOutput` | any JSON | No       | New correct answer for the row. Omit to leave unchanged.                      |
| `metadata`       | any JSON | No       | New metadata cell. Omit to leave unchanged.                                   |
| `custom`         | object   | No       | Custom column values keyed by column identifier. Merged onto existing values. |

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
curl -X PATCH \
  https://api.latitude.so/v1/projects/my-project/datasets/my-dataset/rows/ROW_ID \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "expectedOutput": "The correct answer",
    "custom": { "COLUMN_IDENTIFIER": "high" }
  }'
```

The response carries the new dataset version:

```json theme={"theme":{"light":"github-light","dark":"github-dark"}}
{ "versionId": "os4gxr0mhcocrydibwupq4jk", "version": 10 }
```

### SDKs

<Tabs>
  <Tab title="TypeScript">
    ```typescript theme={"theme":{"light":"github-light","dark":"github-dark"}}
    import { LatitudeApiClient } from "@latitude-data/sdk";

    const client = new LatitudeApiClient({ token: process.env.LATITUDE_API_KEY! });

    const { version } = await client.datasets.updateRow(
      "my-project",
      "my-dataset",
      "ROW_ID",
      {
        expectedOutput: "The correct answer",
        custom: { COLUMN_IDENTIFIER: "high" },
      },
    );
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
    import os

    from latitude_sdk import LatitudeApiClient

    client = LatitudeApiClient(token=os.environ["LATITUDE_API_KEY"])

    result = client.datasets.update_row(
        "my-project",
        "my-dataset",
        "ROW_ID",
        expected_output="The correct answer",
        custom={"COLUMN_IDENTIFIER": "high"},
    )
    ```
  </Tab>
</Tabs>

### From your coding agent (MCP)

Through the [MCP server](../getting-started/mcp), an agent like Claude or Cursor can edit a row for you — for example, *"in my dataset From Traces Drawer, set the expected output of the failing row to …"*. The agent lists the dataset's rows and columns to resolve the ids, then applies the same partial update described above. For the live tool list and full schemas, see the [API reference](https://api.latitude.so/docs).

## Next step

* [Add expected output](./expected-output): the most common reason to edit a row.
* [Custom columns](./custom-columns): add, rename, reorder, and remove the columns a row can hold.
* [Regression testing](../test-and-fix/regression-testing): replay the curated dataset against your agent.