Generative AI Engineer

DATB-GAISD : Generative AI Solution Development

Course Duration

2 Hours

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

This course introduces 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.

What You'll Learn

Introduction to RAG
Preparing Data for RAG Solutions
Vector Search
Assembling and Evaluating a RAG Application

Who Should Attend

Data scientists, machine-learning engineers and AI practitioners aiming to build contextual generative-AI solutions using the RAG (Retrieval-Augmented Generation) method on the Databricks Lakehouse Platform.
Professionals preparing data pipelines, embeddings and vector-search architectures to enable business-specific generative-AI applications.
Practitioners responsible for designing, implementing and evaluating generative-AI workflows that integrate data preparation, vector databases and model–serving components.
Individuals with foundational knowledge of NLP concepts, prompt engineering and the Databricks Data Intelligence Platform, looking to deepen their skills in generative-AI solution development.
Teams intending to move from generative-AI experimentation to production-ready solutions—embedding business context, governance and evaluation into their RAG applications.

Prerequisites

Familiarity with natural language processing concepts
Familiarity with prompt engineering/prompt engineering best practices
Familiarity with the Databricks Data Intelligence Platform

Learning Journey

Coming Soon...

Module 1. Introduction to RAG

What is RAG?
In Context Learning with AI Playground

Module 2. Preparing Data for RAG Solutions

Data Storage and Governance
Data Extraction and Chunking
Embedding Model
Data Preparation in Databricks

Module 3. 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

Module 4. Assembling and Evaluating a RAG Application

MLflow
Evaluating a RAG Application and Continual Learning
Assembling a RAG Application

Frequently Asked Questions (FAQs)

None

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