# Bring your own data

Source: https://redcrown.ai/docs/guides/bring-your-own-data/

Turn your CSV, JSONL, or media files into a scored dataset.

## 1. From a CSV or JSONL

Your data needs two columns: one for the model input and one for the reference output RedCrown scores against.

**UI**

On **Prove** (`/`), paste examples directly into the task input. For a file, use the **Upload** screen (`/advanced/upload`) to import a results file as a ranked run.

**CLI**

From a CSV:

```
redcrown build-dataset --from-csv data.csv --task extract --input-col input --reference-col reference --out task.json
```

From a JSONL file, replace `--from-csv` with `--from-jsonl` and point to your `.jsonl` file.

**MCP**

Pass examples inline in the `examples` argument to `scaffold_experiment` or `prove_task`. No separate dataset build step is needed.

## 2. Audio and images

For transcription or vision tasks, reference media by URL. RedCrown fetches each file at eval time.

**UI**

On **Prove**, attach media files to your examples using the media attachment control. Supported types include audio and images.

**CLI**

After building a dataset with per-item audio references, pass the base URL for your hosted clips:

```
redcrown eval task.json --audio-base https://host/clips/
```

**MCP**

Include input URLs directly in the `examples` array. RedCrown resolves the URLs during the run.

## 3. A public corpus

RedCrown ships two built-in public corpora for benchmarking and reproducibility.

**UI**

Not available via the UI. Use the CLI for public corpora.

**CLI**

Build from the PriMock57 clinical transcription corpus:

```
redcrown build-dataset primock57 --out exp.json
```

Build from an MMLU-Pro reasoning subset:

```
redcrown build-dataset mmlu-pro --out exp.json
```

**MCP**

Not available via MCP. Use the CLI commands above to build corpus datasets.

Last verified 2026-07-02.