# Get started with GenAI

> Get started with GenAI - Learn how to build a GenAI workflow in DataRobot—connect data, compare LLM
> blueprints, evaluate responses, and deploy.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-09-30T19:40:40.828481+00:00` (UTC).

## Primary page

- [Get started with GenAI](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md): Full documentation for this topic (Markdown sidecar).

## Sections on this page

- [How to start?](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#how-to-start): In-page section heading.
- [GenAI workflow](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#genai-workflow-overview): In-page section heading.
- [Create a Use Case and playground](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#get-started): In-page section heading.
- [Create a vector database](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#create-a-vector-database): In-page section heading.
- [Build LLM blueprints](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#build-llm-blueprints): In-page section heading.
- [Chat and compare LLM blueprints](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#chat-and-compare-llm-blueprints): In-page section heading.
- [Use LLM evaluation tools](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#use-llm-evaluation-tools): In-page section heading.
- [Deploy an LLM](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#deploy-an-llm): In-page section heading.
- [What's next?](https://docs.datarobot.com/en/docs/get-started/day0/genai-intro.html.md#whats-next): In-page section heading.

## Related documentation

- [Get started](https://docs.datarobot.com/en/docs/get-started/index.html.md): Linked from this page.
- [First time here?](https://docs.datarobot.com/en/docs/get-started/day0/index.html.md): Linked from this page.
- [Generative AI (GenAI)](https://docs.datarobot.com/en/docs/agentic-ai/index.html.md): Linked from this page.
- [Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html.md): Linked from this page.
- [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html.md): Linked from this page.
- [GenAI basic how-to](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html.md): Linked from this page.
- [sample use cases](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-welcome/index.html.md): Linked from this page.
- [creating a Use Case](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html.md#create-a-use-case): Linked from this page.
- [adding a RAG playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html.md#add-a-playground): Linked from this page.
- [add a vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/index.html.md): Linked from this page.
- [Version the vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html.md#create-a-version): Linked from this page.
- [create an LLM blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html.md): Linked from this page.
- [send it prompts](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html.md): Linked from this page.
- [comparison tool](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html.md): Linked from this page.
- [best practices for prompt engineering](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/prompting-reference.html.md#best-practices-for-prompt-engineering): Linked from this page.
- [metrics and compliance tests](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html.md): Linked from this page.
- [send it](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html.md): Linked from this page.
- [Registry workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/index.html.md): Linked from this page.
- [Compare RAG pipelines with evaluation and governance](https://docs.datarobot.com/en/docs/get-started/how-to/genai-space.html.md): Linked from this page.

## Documentation content

> [!NOTE] Premium
> DataRobot's GenAI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience.

[Generative AI (GenAI)](https://docs.datarobot.com/en/docs/agentic-ai/index.html.md) in Workbench lets you experiment with LLMs, vector databases, and embeddings, then govern and operate the result in production. Use hosted models or bring your own libraries, LLMs, vector databases, and third-party tools. You can work from the UI or the API.

GenAI produces output from a single inference call: prompt in, completion out. The model's output is non-deterministic, meaning the same prompt can produce different (though similarly valid) results across runs, because the model is sampling from a distribution rather than following fixed rules.

However, success depends on more than the model. Grounding data (when you use a vector database), prompting strategy, evaluation, monitoring, and governance all affect whether a workflow is ready for production.

The same path used for predictive models applies to GenAI: experiment in a playground, register in [Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html.md), and monitor in [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html.md).

## How to start?

- For a hands-on walkthrough, try the GenAI basic how-to .
- See the full GenAI and agentic documentation for bringing your own data and LLMs, working in code, and using NVIDIA NIM.
- Review sample use cases that help illustrate a variety of common business applications.

## GenAI workflow

A typical generative workflow includes:

- Creating and versioning vector databases (optional).
- Creating LLM blueprints .
- Chatting with and comparing LLM blueprints.
- Applying evaluation metrics and compliance tests .
- Preparing an LLM blueprint for deployment .

### Create a Use Case and playground

Start by [creating a Use Case](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html.md#create-a-use-case) and [adding a RAG playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html.md#add-a-playground). A playground is the LLM experimentation environment in Workbench, where you build, compare, evaluate, and prepare blueprints for deployment.

### Create a vector database

After the playground is set up, optionally [add a vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/index.html.md) to enrich prompts with relevant context before they are sent to the LLM. When creating a vector database, you:

- Choose a provider.
- Add data.
- Set a basic configuration and text chunking details.

[Version the vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html.md#create-a-version) so the most up-to-date data is available to ground LLM responses.

### Build LLM blueprints

An LLM blueprint represents the full context needed to generate a response from an LLM. You can compare the resulting output in the playground.

When you [create an LLM blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html.md), the playground opens. Select an LLM to get started and then set the configuration options.

In the configuration panel, optionally add a vector database and set the prompting strategy.

After you save, the new LLM blueprint is listed on the left.

### Chat and compare LLM blueprints

After the LLM blueprint configuration is saved, [send it prompts](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html.md) to determine whether further refinements are needed.

Then add several blueprints and use the [comparison tool](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html.md) to test them with the same prompt. This helps you pick the best LLM blueprint for deployment.

See [best practices for prompt engineering](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/prompting-reference.html.md#best-practices-for-prompt-engineering) when chatting and comparing.

### Use LLM evaluation tools

Using [metrics and compliance tests](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html.md), you can evaluate prompts and responses in the playground and define moderation criteria that detect and block problematic output.

Add metrics before or after configuring LLM blueprints:

**Before:**
[https://docs.datarobot.com/en/docs/images/gen-fund-9.png](https://docs.datarobot.com/en/docs/images/gen-fund-9.png)

**After:**
[https://docs.datarobot.com/en/docs/images/gen-fund-10.png](https://docs.datarobot.com/en/docs/images/gen-fund-10.png)


Add evaluation datasets, or generate a synthetic dataset from within DataRobot, to systematically assess how well the model performs for its intended tasks.

Combine evaluation metrics and an evaluation dataset to automate detection of compliance issues through test prompt scenarios. Use DataRobot-supplied evaluations or create your own.

### Deploy an LLM

Once you are satisfied with the LLM blueprint, [send it](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html.md) to the Registry workshop from the playground.

The [Registry workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/index.html.md) is where you test the LLM custom model and deploy it to [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html.md) for monitoring and management.

## What's next?

- Try the GenAI basic how-to
- Compare RAG pipelines with evaluation and governance ( video )
- Review sample GenAI use cases
- Watch a GenAI workflow overview
- Browse the full GenAI and agentic documentation
- Explore GenAI accelerators on GitHub