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Generative AI

Availability information

DataRobot's Generative AI 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.

Continuous deployment docs

For the most up-to-date documentation on DataRobot GenAI capabilities, see the public documentation site.

Working with Generative AI (GenAI) in DataRobot can include creating vector databases, creating and comparing LLM blueprints in the playground, preparing LLM blueprints for deployment, working with metrics, and bringing your own LLM.

See the list of considerations to keep in mind when working with DataRobot GenAI.

Topic Describes...
GenAI in DataRobot overview An overview of the DataRobot GenAI workflow.
Create vector databases Creating the vector database configuration.
Work in the playground Create LLMs, compare chats, apply metrics, and fine-tune results.
Deploy LLMs to production Registering LLMs for deployment.
End-to-end code-first generative AI experimentation A comprehensive overview of the generative AI features DataRobot has to offer with the Python SDK.
Create external vector databases with code How to build, validate, and register an external vector database to the DataRobot platform using DataRobot's Python SDK.
Create external LLMs with code How to set up and validate an external LLM using DataRobot's Python SDK.
Feature considerations Criteria to keep in mind when working with DataRobot GenAI AI capabilities.
Troubleshooting Error handling for common playground and VDB situations.

Updated May 16, 2024