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06 GenAI on Databricks: Assembling and Evaluating a Retrieval-Augmented Generation (RAG) Application with Databricks
A detailed tutorial on implementing a complete Retrieval-Augmented Generation (RAG) pipeline on Databricks, covering everything from setting up vector search and foundation models to deploying the sol
ution in production. The guide provides hands-on instructions for building, evaluating, and registering a RAG application that combines document retrieval with generative AI capabilities.

Reasons to Read -- Learn:

  • how to build a production-ready RAG pipeline that integrates vector search, embeddings, and foundation models on the Databricks platform, with practical code examples and implementation steps.
  • how to properly evaluate and register RAG applications using MLflow, enabling you to track model performance and manage deployments in a production environment through Unity Catalog.
  • specific technical implementation details of RAG components, including how to set up retrieval systems, configure foundation models, and create enriched prompts for better AI responses.
    • publisher: @infinitylearnings1201
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