> ## Documentation Index
> Fetch the complete documentation index at: https://agent-observability-docs.splunk.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Sign up, configure your first integration, and launch a Luna Studio training run.
This walkthrough takes you from a fresh sign-up through a custom evaluator training run. If the resulting evaluator passes registration preflight and your deployment has a Splunk Agent Observability connection, you can also publish it to the Splunk Agent Observability evaluators store.
## Before you start
**Enterprise tier and your own deployment.** Luna Studio is part of the enterprise tier of Splunk Agent Observability and is deployed by Splunk Agent Observability into your own cluster or cloud. See [Availability and deployment](/luna-studio/ui/availability), or [contact
us](https://www.splunk.com/en_us/about-splunk/contact-us.html) to get started.
You'll need:
* An instance of Luna Studio deployed for your organization (Luna Studio is part of the enterprise tier — see [Availability and deployment](/luna-studio/ui/availability)).
* An email address for the Luna Studio account.
* An API key for at least one supported LLM provider: [OpenAI](https://platform.openai.com/api-keys), [Anthropic](https://platform.claude.com/settings/keys), [Google AI Studio](https://aistudio.google.com/apikey), or [Groq](https://console.groq.com/keys).
* 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.
Don't have a labelled training dataset yet? Luna Studio can **generate a training set** from your test set — see the [training-set step below](#step-6-add-a-training-set). The test set still needs enough labelled rows for reliable
evaluation.
## Walkthrough
Open your organization's Luna Studio URL and create an account.
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Enter an email and a password (8+ characters), accept the terms, and click **Create account**.
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.
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.
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On the project page, click **New run** in the top right. The run creation flow opens at **Step 1 — Evaluator**.
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](/luna-studio/ui/runs/new-run/step-1-evaluator) for the full reference.
Click **Next step**.
Pick an existing test set from the dropdown, or click **Add new test set** to upload one.
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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**.
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**.
Review the run summary. The base model is selected from the models configured for your organization.
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Click **Confirm and launch**. The run enters the **Queued** state and you're routed back to the project page.
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.
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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](/concepts/evaluators/overview) and the run's status flips to **Registered**. If the action is disabled, use its blocker tooltip and [registration prerequisites](/luna-studio/ui/runs/register-evaluator#prerequisites) to identify the unsupported contract or missing integration.
You've now created your first custom Luna evaluator. If you completed the optional registration step, it is also published to Splunk Agent Observability.
## What's next
Understand the relationships between projects, runs, evaluators, and datasets.
Deep dive on every step of the new run flow.
Manage your test sets and training sets.
Add provider credentials for the models and platform features your team uses.