Building Retrieval-augmented Generation (RAG) Pipelines with Databricks

Building production-ready GenAI applications requires more than connecting a model to data.

What you'll learn
Building production-ready GenAI applications requires more than connecting a model to data.
In this course, Building Retrieval-augmented Generation (RAG) Pipelines with Databricks, you’ll learn how to build end-to-end Retrieval-augmented Generation (RAG) solutions using Databricks technologies and enterprise data.
First, you’ll explore what RAG is and why it’s important for enterprise GenAI solutions.
Next, you’ll discover how to prepare enterprise data using embeddings, store it in Unity Catalog Delta tables, and index it with Databricks Vector Search.
Then, you’ll study how to build an end-to-end RAG pipeline and register and evaluate it using MLflow.
Finally, you’ll learn how to build RAG workflows using Agent Bricks in Databricks.
When you’re finished with this course, you’ll have the skills and knowledge of RAG and Databricks features needed to build GenAI solutions on top of enterprise data.

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