Quickstart

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Before you start

• An instance of Luna Studio deployed for your organization (Luna Studio is part of the enterprise tier — see Availability and deployment).
• An email address for the Luna Studio account.
• An API key for at least one supported LLM provider: OpenAI, Anthropic, Google AI Studio, or Groq.
• A labelled CSV test dataset with the columns your evaluator requires, at least 300 rows total, and at least 100 examples per class. The source picker can ingest JSONL, but current run processing requires CSV.
• Optional for training, required for import and registration: a Splunk Agent Observability API connection configured by your Luna Studio deployment operator.

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Walkthrough

1

Sign up

Open your organization’s Luna Studio URL and create an account.

Enter an email and a password (8+ characters), accept the terms, and click Create account.

2

Connect your first LLM provider

On first launch, Luna Studio drops you into the onboarding wizard. Pick a supported LLM provider, such as OpenAI or Anthropic, and click Add integration on the card.A modal opens for the provider’s API key. Paste it and click Save changes.If you plan to import from Splunk Agent Observability or register the result, confirm that your deployment operator configured the Splunk Agent Observability connection. The current backend uses the deployment’s Splunk Agent Observability API URL and key for those operations.

3

Create your first project

Click Continue and enter a project name (e.g. support-tone-classifier). Click Continue again — Luna Studio creates the project and routes you to its Training runs page.

4

Start a new training run

On the project page, click New run in the top right. The run creation flow opens at Step 1 — Evaluator.

5

Pick an evaluator (Step 1)

Pick an evaluator from the dropdown. The list includes Splunk Agent Observability presets, custom Splunk Agent Observability evaluators, and saved custom prompts that are trainable in Luna Studio.Or click the dropdown’s Use custom prompt option to write your own LLM-as-judge prompt. See Step 1: Evaluator for the full reference.Click Next step.

6

Add a test set (Step 2)

Pick an existing test set from the dropdown, or click Add new test set to upload one.

Test sets need the feature columns required by your evaluator shape, plus a label column. Because you are adding the dataset inside the run wizard, Luna Studio runs evaluator-aware validation before attaching it to the run. Resolve any highlighted errors before continuing.Click Next step.

7

Add a training set (Step 3)

Choose a training source:

• Generate from test set (recommended for a first run) — Luna Studio uses 20% of your test set as seed examples and targets 2,000 synthetic labelled examples using your LLM-as-judge prompt from Step 1. Enhancement examples can be appended, so the final row count may be higher.
• Add training logs — upload or import your own production logs.
• Use existing training set — reuse a training dataset that already exists in your workspace. If you pick Generate, a drawer opens. Pick a model from your configured providers and click Generate sample dataset. Data generation moves through Queued → Generating data → Data ready inside Step 3. Review the sample rows, select any that should steer regeneration, and click Generate final dataset. Wait for the final dataset to be ready before continuing. Click Next step.

8

Confirm and launch (Step 4)

Review the run summary. The base model is selected from the models configured for your organization.

Click Confirm and launch. The run enters the Queued state and you’re routed back to the project page.

9

Wait for training to complete

Click the run row to open its details page and see live status. After Confirm and launch, the fine-tuning run follows Queued → Training → Fine-tuned. Any Generating data and Data ready states occurred inside Step 3 before launch.Once the run reaches Fine-tuned, the run details page shows a evaluators grid (F1 score, AUC-ROC, etc.) versus a baseline.

10

Register your evaluator

If the registration preflight passes, the Register evaluator button in the run details footer is enabled. Enter an evaluator name and click Register.After registration succeeds, the evaluator appears in the Splunk Agent Observability evaluators store and the run’s status flips to Registered. If the action is disabled, use its blocker tooltip and registration prerequisites to identify the unsupported contract or missing integration.

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What’s next