Retrieval Augmented Generation (RAG)

In this exercise, we will learn how to build a Retrieval Augmented Generation (RAG) system from scratch.

In this tutorial, you will learn:

• how RAG works
• full pipeline implementation
• chunking and embeddings strategies
• modular architecture
• production-ready workflow

Note: RAG powers most real-world AI applications today.

What is RAG?

Definition: Retrieval Augmented Generation (RAG) improves LLM responses by retrieving relevant information from an external knowledge base before generating an answer.

In simple words:

LLM + your data = accurate answers

Why RAG is Needed

Problem 1 — Hallucination

LLMs may generate incorrect answers when data is missing.

Example:

• Model trained until Aug 1
• You ask about Aug 15 event
• model invents an answer

Problem 2 — No Access to Private Data

Your company data may include:

• HR policies
• finance documents
• internal SOPs

• not in LLM training data
• fine-tuning is expensive

First Solution: Fine-Tuning

What is Fine-Tuning? Fine-tuning means retraining a pretrained model on domain-specific data.

Goal: Add domain knowledge to the model.

Example Analogy

• Engineering degree → pretraining
• Company training → fine-tuning

Problems with Fine-Tuning

• Expensive
• Training large models costs money
• Requires expertise
• Needs ML engineers and infrastructure
• Hard to update
• New data means retraining again
• Not ideal for frequently changing data

So we need a better solution.

RAG Solves Both

• reduces hallucination
• uses real-time and private data
• avoids expensive fine-tuning
• updates instantly when data changes

Traditional LLM vs RAG

Traditional LLM Flow

User Query → Prompt → LLM → Answer

Problems:

• outdated knowledge
• hallucinations
• no private data

RAG Flow

User Query → Retrieve relevant data → Provide context to LLM → Generate accurate answer

RAG Architecture Overview

RAG has two main pipelines:

1. Data Injection Pipeline

   This pipeline prepares documents and stores them in a searchable format.

2. Retrieval Pipeline

   This pipeline retrieves relevant information when a user asks a question.

Pipeline 1: Data Injection Pipeline

Step-by-step:

1. Data Sources
   • PDF
   • HTML
   • Excel
   • SQL
   • JSON
   • text files
2. Data Parsing
   • Extract readable text
   • improves retrieval accuracy
   • handles structured and unstructured data
3. Chunking (Very Important)
   • Large documents are divided into smaller chunks
   • fits LLM context size
   • improves retrieval precision
   • reduces memory usage
4. Embeddings
   • Convert text into vectors
   • allows similarity search
   • provides semantic understanding
   • OpenAI
   • Gemini
   • Hugging Face
   • open-source models
5. Vector Database
   • Stores vector embeddings
   • ChromaDB
   • FAISS
   • Pinecone
   • Weaviate

Result: You now have a searchable knowledge base.

Retrieval Pipeline

When a user asks a question, the retrieval pipeline works as follows:

1. Convert query into embedding
2. Search the vector database
3. Retrieve the most relevant context
4. Send context and prompt to the LLM
5. LLM generates the final answer

Example

User asks: What is leave policy?

System:

• finds HR policy chunk
• sends it to the LLM
• returns accurate answer

Core RAG Formula

Workflow

User Query
   ↓
Embedding
   ↓
Vector Search
   ↓
Relevant Context
   ↓
Prompt + Context
   ↓
LLM Response

Key Concept: Context Augmentation

Context augmentation means adding retrieved context before the LLM generates the response.

• adds retrieved context
• guides LLM response

Without context, the model may hallucinate. With context, the answer becomes more accurate.

Document Structure (LangChain Concept)

Documents usually contain two important parts:

• Page Content: Actual text
• Metadata: Extra information

Metadata may include:

• file name
• author
• page number
• source

Why metadata matters?

• You can filter search by author
• You can filter search by file type
• You can filter search by date

Chunking Strategies

Chunking divides documents into smaller pieces.

Common methods:

• fixed-size chunks
• semantic chunking
• recursive splitting

Benefits:

• better retrieval
• efficient embedding
• context optimization

Embeddings Explained

Embeddings convert text into numbers.

Example: "machine learning" → [0.12, 0.98, …]

They are used for:

• cosine similarity
• semantic search

Vector Database Role

A vector database stores embeddings for fast retrieval.

It supports:

• similarity search
• filtering
• ranking

RAG Reduces Hallucination

RAG does not eliminate hallucination fully.

But:

• if data exists, it improves answer accuracy
• if data is missing, the LLM may still hallucinate

Real-World Example

Perplexity AI uses RAG for:

• web retrieval
• context summarization
• citation-based answers

Implementation Workflow

1. Phase 1 — Basic
   • build simple RAG
   • load documents
   • chunk and embed
2. Phase 2 — Intermediate
   • modular code
   • vector search
   • context retrieval
3. Phase 3 — Advanced
   • agentic RAG
   • optimization
   • context engineering

Modular RAG Architecture

Module Structure

📁 RAG System
├── 📁 Data Loader
├── 📁 Chunk and Embedding Module
├── 📁 Vector Store Module
├── 📁 Retriever
└── 📁 LLM Generator

Production systems usually split RAG into multiple modules:

• Data Loader: Reads documents
• Chunk and Embedding Module: Processes text
• Vector Store Module: Stores embeddings
• Retriever: Fetches context
• LLM Generator: Creates answers

Optimization Topics Covered

The course also discusses:

• semantic chunking
• context engineering
• embedding selection
• retrieval accuracy improvements

Why RAG is Important Today

According to industry trends, many enterprise AI applications now use RAG.

Common use cases include:

• enterprise chatbots
• document Q&A systems
• legal research
• developer assistants
• knowledge base search