Focus on this objective today: Optimize through Retrieval Augmented Generation (RAG): Prepare data for RAG, including cleaning, chunking, and embedding. Explain it in your own words, apply it to one realistic scenario, and verify the details against the official source. Source context: Read in English Implement Retrieval Augmented Generation (RAG) with Azure Databricks Module 8 Units Feedback Intermediate Data Engineer Azure Databricks Retrieval Augmented Generation (RAG) is an advanced technique in natural language processing that enhances the capabilities of generative models by integrating external information retrieval mechanisms. When you use both generative models and retrieval systems, RAG dynamically fetches relevant information from external data sources to augment the generation process, leading to more accurate and contextually relevant outputs. Learning objectives In this module, you learn how to: Set up a RAG workflow.
Objective
Optimize through Retrieval Augmented Generation (RAG): Prepare data for RAG, including cleaning, chunking, and embedding