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Overview
This is your introduction to contextual generative AI solutions using the retrieval-augmented generation (RAG) method. First, you’ll be introduced to RAG architecture and the significance of contextual information using Mosaic AI Playground. Next, we’ll show you how to prepare data for generative AI solutions and connect this process with building a RAG architecture. Finally, you’ll explore concepts related to context embedding, vectors, vector databases, and the utilization of Mosaic AI Vector Search.
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Introduction to RAG What is RAG? In Context Learning with AI Playground Preparing Data for RAG Solutions Data Storage and Governance Data Extraction and Chunking Embedding Model Data Preparation in Databricks Vector Search Introduction to Vector Stores Vector Search Process and Performance Choosing the right Vector Database Mosaic AI Vector Search Creating a Vector Search Index Assembling and Evaluating a RAG Application MLflow Evaluating a RAG Application and Continual Learning Assembling a RAG Application
Introduction to RAG
Preparing Data for RAG Solutions
Vector Search
Assembling and Evaluating a RAG Application
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