GenAI Fundamentals: Implementing RAG with Azure Search & OpenAI

GenAI Fundamentals: Implementing RAG with Azure Search & OpenAI
A technical journey through Retrieval Augmented Generation
AS
by Ankit Shivam
Our Journey for next 50 minutes !
Generative AI Fundamentals
Large Language Models
Trained on vast text corpora to predict next tokens
Content Creation
Generate human-like text, images, code on demand
Limitations
Hallucinations, outdated knowledge, no source attribution
Let's Tell A Story !
Retrieval Augmented Generation (RAG)
Retrieve
Find relevant documents from knowledge base
Augment
Add context to user's query
Generate
LLM creates response using retrieved information
Basic RAG Pattern 
Returning to Our Tale ..
Understanding Vector Embeddings
Understanding Meaning From Numbers
1
Words to Numbers
System doesn't understand words - it understands numbers. 
It turns text into list of numbers(vector). 
2
Mapping Meanings
Similar words and ideas get mapped to similar numbers in the vector space. 
"Reset password" and "recover account" become close together.
3
Helps RAG find the right document 
It doesn't search by keyword -it serches by meaning
Core Components of RAG System
Storage
Where documents live (e.g., PDFs in Azure Blob Storage)
Embedding Model
Converts text into vectors (e.g., OpenAI’s Ada-002)
Search & Indexing Service
Finds relevant content using keyword and vector search (e.g., Azure AI Search)
Large Language Model (LLM)
Generates response using retrieved content (e.g , GPT-4)
Orchestrator
Puts all pieces together to serve end user request (e.g., Azure Functions)
Next Steps & Demo Preparation
Environment Setup
Create Azure resources with proper permissions
Install required Python libraries and SDKs
Data Preparation
Gather sample documents for the knowledge base
Process and index content before demo
Demo Scenarios
Prepare example queries showing RAG advantages
Compare with standard LLM responses
Power of Better Retrieval : A Happy Ending !