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If your question isn’t answered here, check Troubleshooting for runtime issues.

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General

Luna Studio is Galileo’s web app for fine-tuning custom evaluation metrics for LLM applications. You bring a human-labelled test set, optionally generate a training set, fine-tune a Luna base model, and register eligible metrics in the Galileo metrics store. See Welcome for the longer pitch.
Data scientists, ML engineers, and AI engineers who need evaluation metrics tailored to a specific domain (legal, healthcare, RAG over internal docs, etc.). Use the Luna Studio UI for a guided, no-code workflow. Use the Luna Studio SDK when you want more control or need to run fine-tuning on your own infrastructure.
Galileo is the broader platform — evaluation, observability, guardrails. Luna Studio is the metric-fine-tuning workspace inside Galileo. Metrics produced in Luna Studio are registered to the Galileo metrics store, where they’re usable across the rest of the platform.
Luna Studio is part of the enterprise tier of Galileo and is deployed by Galileo into your own cluster or cloud. See Availability and deployment, or contact us to get started.

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Test sets and training sets

Aim for 1,000-3,000 representative samples with a good class distribution when possible. Luna Studio enforces a minimum of 300 human-labelled rows total and at least 100 samples per class.
Not necessarily. If you have a training set, then sure, but if not, you can choose to generate data using Generate from test set — Luna Studio synthetically generates a training set from 20% of your test set. See Step 3.Upload your own training set when you have labelled production logs that better represent the distribution you want to evaluate.
Yes, for uploaded or imported logs. If validation finds that the label column is missing, choose Label with metric prompt instead, configure the provider and model, and start the labelling pass. Luna Studio saves the labelled result as a training dataset and uses that result for training. Generated training sets are always labelled.
Yes. Datasets are organization-wide, not project-scoped. Once you’ve added a test set, every project in your organization can use it.
CSV is the current end-to-end format for run validation and processing. The source picker accepts .jsonl during ingestion, but downstream run processing reads CSV, so convert JSONL to CSV before using it in a run. See Add a dataset.

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Metrics

Predefined metrics use battle-tested LLM-as-judge prompts curated by Galileo (e.g. Toxicity, Context adherence). Custom metrics let you write your own prompt. Both fine-tune the same way.
Luna Studio now describes this contract with Input level and Metric shape. The input level identifies where the metric evaluates data, while the metric shape identifies the required dataset columns. See Custom prompts in Step 1.
No. Once registered, the metric is snapshotted in the Galileo metrics store. To iterate, launch a new run with the same metric template and register it under a new name (or unregister the old one in Galileo first).

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Training

Depends on the base model, training set size and GPU availability. Most runs take a few hours, and larger models or larger datasets can take longer.
Not from the current UI after fine-tuning has been launched. You can cancel a draft from the run wizard, including while training data is queued or being generated. The run details header and Training runs table do not currently expose cancellation for Queued or Training runs. A run can still appear as Cancelled when an API or the underlying platform records a cancellation. See Run lifecycle.

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Integrations

The current UI can configure OpenAI, Anthropic, Google Gemini, and Groq. Galileo is a separate deployment connection for dataset import and metric registration. Other provider cards are marked Coming soon. See Integrations overview.
Not through the current Luna Studio UI. The Custom card is marked Coming soon and does not accept credentials yet. Contact your deployment administrator or Galileo representative about deployment-specific model access.

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Where to go next

Troubleshooting

Runtime errors and how to recover.

Quickstart

End-to-end walkthrough.