Collect feedback from 🧠 Expert Users


This feature is in Private Preview. To try it, reach out to your Databricks contact.

Looking for a different RAG Studio doc? Go to the RAG documentation index

This tutorial walks you through deploying your RAG Application to the Reviewers Environment in order to allow 🧠 Expert Users to test the application and provide feedback.

Step 1: Create the Reviewers & End Users Environment

  1. If you did not already run this command in Initialize a RAG Application, run the following command to initialize these Environments. This command takes about 10 minutes to run.

    ./rag setup-prod-env


    See Infrastructure and Unity Catalog assets created by RAG Studio for details of what is created in your Workspace and Unity Catalog schema.

  2. Run the following command to deploy the version to the Reviewers Environment. This command takes about 10 minutes to run.

    ./rag deploy-chain -v 1 -e reviewers
  3. In the console, you will see output similar to below. Open the URL in your web browser to open the 💬 Review UI. You can share this URL with your 🧠 Expert Users.

    ...truncated for clarity of docs...
    Task deploy_chain_task:
    Your Review UI is now available. Open the Review UI here: https://<workspace-url>/ml/review/model/catalog.schema.rag_studio_databricks-docs-bot/version/1/environment/reviewers
  4. Add permissions to the deployed version so your 🧠 Expert Users can access the above URL.

    • Give the Databricks user you wish to grant access read permissions to

      • the MLflow Experiment

      • the Model Serving endpoint

      • the Unity Catalog Model


    🚧 Roadmap 🚧 Support for adding any corporate SSO to access the 💬 Review UI e.g., no requirements for a Databricks account.

  5. Now, every time a 🧠 Expert Users chats with your RAG Application, the 🗂️ Request Log and 👍 Assessment & Evaluation Results Log will be populated.

Follow the next tutorial!

Create an 📖 Evaluation Set