Step 3 — Training set

Generate a training set from your test set or add your own training logs.

The training set is the dataset that fine-tunes your base model. Generated training sets contain 2,000 labelled examples, and you can also add or reuse your own training data.

Step 3 — pick a training data source: generate, add logs, or use an existing training set

The three training dataset sources

The step opens with a section heading Training data source and three selectable cards:

Generate from test set

Uses 20% of your test set as seed examples to generate 2,000 labelled training examples. Recommended for first runs.

Add training logs

Upload or import your own raw production logs. If they are unlabelled, Luna Studio labels them before training.

Use existing training set

Reuse a previously generated, labelled, or uploaded training dataset from your workspace.

Pick one — clicking a card opens the next step for that source.

Generate from test set

This option creates a training set from your test set in three steps:

1. Configure generation and generate a sample dataset
2. Review 50 sample rows and provide feedback to the generator
3. Generate the final 2,000-example dataset once you are happy with the samples

Configure the generator

Configure generation with the following settings:

Generate from test set drawer, configure phase — pick a model and dataset name before generating samples

Field	Notes
Test set (read-only)	Shows your selected test set with the caption “Uses 20% of test set as seed examples”.
Model	The LLM that generates the training samples. Options depend on the providers you have configured. Larger models usually produce better training data.
Output dataset name	Provide a name for the output dataset like project-ABC-metric-PQR-training-set-v1. Defaults to generated-training-set.
Metric (read-only)	Includes a View prompt popover so you can re-check the metric prompt.
Advanced settings	Optional generation settings. Keep the defaults for first runs.

Click Generate sample dataset at the bottom of the drawer, once you are happy with the settings.

Review the sample data

Generate from test set drawer, review phase — approve sample rows or regenerate before kicking off the full run

You’re reviewing 50 sample rows before kicking off the full generation.

Provide feedback and Regenerate samples

You can provide feedback by selecting the rows that look wrong and clicking the Regenerate button. Once you click the button, the Regenerate dataset modal opens with a radio group of reasons:

Reason	When to pick it
Samples are too repetitive	The generated rows look almost identical to each other.
Labels look incorrect	The labels don’t match what the inputs deserve.
Inputs are off-topic	The inputs don’t reflect the kind of data your application sees.
Provide own feedback	Free-form text area reveals — describe what’s wrong in your own words.

Click Regenerate to kick off another sample generation.

The Regenerate button in the modal stays disabled until either a reason is picked or, for “Provide own feedback”, the text is non-empty.

Note: you can provide feedback up to three times. You can also track the cycles in the UI.

Generate the final dataset

Once you’re happy with the samples, the footer button changes to Generate final dataset. Clicking it creates the full 2,000-example training set. When it completes, the drawer closes and Step 3 shows the Training set completed view (see below).

Add training logs

The Add training logs path uploads or imports your own production logs.

Clicking the card opens the Add training set modal — the same generic dataset source modal used elsewhere in the app, with three sources:

Upload from local

Drag-and-drop a .csv or .jsonl file.

Fetch from URL

Paste an http://, https://, s3://, or gs:// URL.

Import from Galileo

Browse datasets in your connected Galileo workspace.

If the logs are missing labels, Luna Studio opens the label-only generation flow. It uses your metric prompt to label the logs, saves a labelled training dataset, and then uses that dataset for training.

Use existing training set

The Use existing training set path lets you pick a previously generated, labelled, or uploaded training dataset from this workspace without regenerating data or importing a new file.

Validation

Luna Studio runs validation on the training set to ensure it meets the required schema / format / content rules. If there are any validation errors, they will be highlighted (See example below).

For more details, see Validation.

Training set completed

After either flow finishes, the step replaces the picker with a Selected dataset card and (if available) a preview table.

Step 3 once a training set is selected — the picker collapses into a card with a row preview

Where to go next

Step 4 — Config and launch

Pick a base model and launch.

Training sets reference

Schema, validation rules, and sources.