Generate RAG Responses

Interacting with a Stored RAG Pipeline in GenAI Studio

Retrieval-Augmented Generation (RAG) combines the power of a language model with external document retrieval to provide richer and more accurate responses based on a broader knowledge base.


  • Access to a stored RAG pipeline connected to a data vector store.

How to Generate Responses Using a RAG Pipeline

Navigating to GenAI from a Cluster
If you are starting from an Machine Learning Development Environment cluster, select GenAI near the lower left corner to open GenAI Studio in your browser.
  • Navigate to GenAI Studio.
  • Select Create a new project.
  • Provide a name and then select Create. You’ll see the new project in your project list.
  • Select your project from the project list to open it.
  • In the Snapshots tab of your project, select New Playground to open a new snapshot Playground.
  • In the playground, choose Document Retrieval from the playground type.
  • In Select RAG, choose your RAG pipeline and then select Load Now.
  • In the input prompt, type a question and then select Generate.
  • GenAI Studio displays a response and the documents used to generate it.
Snapshot Mode
RAG snapshots are easy to identify. Once you’ve created a document retrieval snapshot, the mode column will display rag.