Generative AI Solution Development
Contact us to book this course
Learning Track
Generative AI
Delivery methods
On-Site, Virtual
Duration
1/2 day
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.
Objectives
- Describe RAG architecture.
- Use Mosaic AI Playground to explore the significance of contextual information.
- Prepare data for generative AI solutions.
- Connect data preparation for generative AI solutions to building a RAG architecture.
- Describe fundamental concepts about context embedding, vectors, vector databases, and the utlization of Mosaic AI Vector Search.
Prerequisites
- Familiarity with natural language processing concepts
- Familiarity with prompt engineering/prompt engineering best practices
- Familiarity with the Databricks Data Intelligence Platform
Course outline
- What is RAG?
- In Context Learning with AI Playground
- Data Storage and Governance
- Data Extraction and Chunking
- Embedding Model
- Data Preparation in Databricks
- Introduction to Vector Stores
- Vector Search Process and Performance
- Choosing the right Vector Database
- Mosaic AI Vector Search
- Creating a Vector Search Index
- MLflow
- Evaluating a RAG Application and Continual Learning
- Assembling a RAG Application
