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Generative AI Solution Development

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.


Languages Available: English | 日本語 | Português BR | 한국어

Skill Level
Associate
Duration
4h
Prerequisites
  • Familiarity with natural language processing concepts
  • Familiarity with prompt engineering/prompt engineering best practices 
  • Familiarity with the Databricks Data Intelligence Platform


Outline

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

Upcoming Public Classes

Date
Time
Language
Price
Feb 17
08 AM - 12 PM (America/New_York)
English
$750.00
Feb 19
01 PM - 05 PM (Europe/London)
English
$750.00
Feb 21
01 PM - 05 PM (Asia/Kolkata)
English
$750.00
Mar 11
09 AM - 01 PM (Asia/Kolkata)
English
$750.00
Mar 11
09 AM - 01 PM (Europe/London)
English
$750.00
Mar 13
01 PM - 05 PM (America/New_York)
English
$750.00
Apr 14
01 PM - 05 PM (Europe/London)
English
$750.00
Apr 16
09 AM - 01 PM (America/New_York)
English
$750.00
Apr 18
01 PM - 05 PM (Asia/Kolkata)
English
$750.00

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