# Generative AI

> Generative AI - How to build, validate, and deploy generative AI applications using LLMs, vector
> databases, and moderation tools. This section covers creating LLM blueprints, managing chats and
> prompts, setting up vector databases for RAG (Retrieval-Augmented Generation), and implementing
> safety measures through moderation.

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 `2026-04-24T16:03:56.279336+00:00` (UTC).

## Primary page

- [Generative AI](https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/index.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [Python API client user guide](https://docs.datarobot.com/en/docs/api/dev-learning/python/index.html): Linked from this page.
- [LLM blueprints](https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/llm-blueprints.html): Linked from this page.
- [Validate custom model LLMs](https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/custom-model-validation.html): Linked from this page.
- [Vector databases](https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/vector-databases.html): Linked from this page.
- [Chats and prompting](https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/chats-prompting.html): Linked from this page.
- [End-to-end RAG application workflow](https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/rag-workflow.html): Linked from this page.

## Documentation content

# Generative AI

How to build, validate, and deploy generative AI applications using LLMs, vector databases, and moderation tools. This section covers creating LLM blueprints, managing chats and prompts, setting up vector databases for RAG (Retrieval-Augmented Generation), and implementing safety measures through moderation.

| Topic | Description |
| --- | --- |
| LLM blueprints | Create and manage LLM blueprints that define how large language models are configured and used in your applications. |
| Validate custom model LLMs | Validate deployments as custom model LLMs before using them in LLM blueprints. |
| Vector databases | Set up and manage vector databases for RAG (Retrieval-Augmented Generation) workflows. |
| Chats and prompting | Manage chat sessions and interact with LLMs through prompts, maintaining conversation history. |
| End-to-end RAG application workflow | A complete example of building a RAG application from scratch. |