DEV Community: Pavan Barnana

RAG (Retrieval-Augmented Generation) Explained for Beginners: Build AI Applications Using Your Own Data

Pavan Barnana — Fri, 12 Jun 2026 02:01:03 +0000

Introduction

Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude are incredibly powerful. They can answer questions, generate code, summarize documents, and assist with various tasks.

However, they have one major limitation:

They only know what they were trained on.

If you ask them about your company's internal documents, private PDFs, or the latest information that wasn't part of their training data, they may provide incorrect answers or simply not know the answer.

This is where RAG (Retrieval-Augmented Generation) comes into the picture.

RAG enables AI applications to retrieve relevant information from external data sources and use that information to generate accurate responses.

In this blog, we will learn what RAG is, how it works, and why it has become one of the most important techniques in modern AI applications.

What is RAG?

RAG stands for Retrieval-Augmented Generation.

It is a technique that combines:

Information Retrieval
Large Language Models (LLMs)

Instead of asking the LLM to answer solely from its training data, we first retrieve relevant information from our own documents and then provide that information to the LLM.

The LLM uses this retrieved context to generate a more accurate response.

Simple Example

Imagine you have:

Employee handbook
Company policies
Product documentation
Internal knowledge base

A user asks:

"What is our company's work-from-home policy?"

Without RAG:

The AI may not know the answer.
It may generate a generic response.

With RAG:

The system searches company documents.
Finds the work-from-home policy.
Sends the relevant content to the LLM.
The LLM generates an accurate answer based on company data.

Why Do We Need RAG?

Traditional LLMs face several challenges:

1. Outdated Knowledge

Training an LLM takes a lot of time and resources.

The model may not know recent updates.

2. Hallucinations

Sometimes AI confidently provides incorrect answers.

3. No Access to Private Data

LLMs do not automatically know:

Company documents
Internal wikis
Private PDFs
Enterprise databases

4. Expensive Fine-Tuning

Fine-tuning a model every time data changes is costly.

RAG solves all these problems efficiently.

How RAG Works

The RAG workflow consists of two major phases:

Phase 1: Data Preparation

Step 1: Collect Data

Data can come from:

PDFs
Word documents
Websites
Databases
APIs

Example:

Employee handbook.pdf
HR policies.pdf
Product documentation.pdf

Step 2: Text Extraction

The content is extracted from these documents.

Example:

Original PDF:

"Employees may work remotely for up to three days per week."

Extracted text:

"Employees may work remotely for up to three days per week."

Step 3: Chunking

Large documents are divided into smaller pieces called chunks.

Example:

Chunk 1:
"Employees may work remotely..."

Chunk 2:
"Leave policy details..."

Chunk 3:
"Health insurance information..."

This makes searching much more efficient.

Step 4: Generate Embeddings

The chunks are converted into numerical vectors.

Example:

Text:

"Employees may work remotely."

Embedding:

[0.12, -0.45, 0.78, ...]

These vectors help computers understand semantic meaning.

Step 5: Store in Vector Database

The embeddings are stored in a vector database.

Popular vector databases:

ChromaDB
Pinecone
Weaviate
FAISS

At this point, the system is ready to answer questions.

Query Processing Phase

Now imagine a user asks:

"Can employees work from home?"

Step 1: Convert Question to Embedding

The user's question is converted into a vector.

Step 2: Similarity Search

The vector database finds the most relevant chunks.

Example Retrieved Chunk:

"Employees may work remotely for up to three days per week."

Step 3: Send Context to LLM

Prompt:

Question:
Can employees work from home?

Context:
Employees may work remotely for up to three days per week.

Step 4: Generate Final Answer

The LLM generates:

"Yes. According to company policy, employees may work remotely for up to three days per week."

This answer is based on actual company data.

RAG Architecture

You can use the architecture diagram below in your blog:

Data Sources
(PDFs, Websites, Documents)

↓

Text Extraction

↓

Chunking

↓

Embeddings

↓

Vector Database

↓

User Question

↓

Retriever

↓

Relevant Chunks

↓

LLM

↓

Final Answer

Key Components of RAG

1. Data Sources

Knowledge repositories containing information.

Examples:

PDFs
Websites
Databases
Internal documents

2. Embedding Model

Converts text into vectors.

Popular options:

OpenAI Embeddings
BGE Embeddings
Sentence Transformers

3. Vector Database

Stores embeddings and performs similarity search.

Examples:

Pinecone
Chroma
FAISS
Weaviate

4. Retriever

Finds the most relevant information for a query.

5. LLM

Generates the final response.

Examples:

GPT-4
Llama
Gemini
Claude

Advantages of RAG

Reduced Hallucinations

The model relies on retrieved information.

Real-Time Updates

Update documents without retraining the model.

Lower Cost

No need for frequent fine-tuning.

Enterprise Friendly

Works perfectly with company knowledge bases.

Real-World Use Cases

Enterprise Knowledge Assistant

Employees can ask questions about company policies.

Customer Support Chatbots

Answer customer questions using product documentation.

Legal Document Search

Retrieve information from contracts and legal records.

Healthcare Assistants

Provide answers using medical guidelines.

Educational Platforms

Answer questions from textbooks and study materials.

Tech Stack for Building a RAG Application

A typical RAG application can be built using:

Backend:

Python
FastAPI

LLM:

OpenAI GPT
Llama

Framework:

LangChain
LlamaIndex

Vector Database:

ChromaDB
Pinecone
FAISS

Frontend:

React
Angular

Enterprise Backend Alternative:

Spring Boot + Python AI Service

Conclusion

Retrieval-Augmented Generation (RAG) is one of the most powerful techniques in modern AI development.

Instead of depending solely on an LLM's training data, RAG allows applications to retrieve relevant information from external knowledge sources and generate accurate, context-aware responses.

Whether you are building a customer support chatbot, enterprise knowledge assistant, document search engine, or AI-powered application, RAG provides a scalable and cost-effective solution.

As AI adoption continues to grow, understanding RAG is becoming an essential skill for software engineers and AI developers.

In the next blog, we will build a complete RAG-based Enterprise Knowledge Assistant using Spring Boot, Python, LangChain, ChromaDB, and OpenAI.

Low-Level System Design (LLD) in Java: A Beginner's Guide

Pavan Barnana — Thu, 11 Jun 2026 16:31:34 +0000

Introduction

When preparing for software engineering interviews, many developers focus heavily on Data Structures and Algorithms. However, as you move toward mid-level and senior roles, Low-Level Design (LLD) becomes equally important.

Low-Level Design focuses on designing classes, objects, relationships, and interactions within a system. It helps developers create maintainable, scalable, and extensible applications.

In this blog, we'll understand the fundamentals of LLD and see how Java can be used to implement clean object-oriented designs.

What is Low-Level Design?

Low-Level Design is the process of converting business requirements into class-level designs.

It involves:

Identifying classes and objects
Defining relationships between classes
Applying Object-Oriented Programming principles
Using Design Patterns where necessary
Writing maintainable and reusable code

Example

Suppose we are building a Parking Lot System.

Instead of directly writing code, we first identify:

Vehicle
ParkingSpot
ParkingFloor
Ticket
Payment
ParkingLot

Each of these becomes a separate class in our design.

Core Object-Oriented Principles

1. Encapsulation

Encapsulation means hiding internal implementation details and exposing only necessary functionality.

public class BankAccount {

    private double balance;

    public void deposit(double amount) {
        balance += amount;
    }

    public double getBalance() {
        return balance;
    }
}

Benefits:

Better security
Easier maintenance
Controlled access

2. Abstraction

Abstraction hides complexity from users.

public interface Payment {
    void pay(double amount);
}

Users only know how to call pay(), not how the payment is processed.

3. Inheritance

Inheritance promotes code reuse.

public class Vehicle {
    protected String vehicleNumber;
}

public class Car extends Vehicle {
}

4. Polymorphism

Different implementations through a common interface.

Payment payment = new CreditCardPayment();
payment.pay(1000);

The same method behaves differently depending on the implementation.

SOLID Principles

A good LLD follows SOLID principles.

S - Single Responsibility Principle

A class should have only one reason to change.

Bad:

class User {
    void saveUser() {}
    void sendEmail() {}
}

Good:

class UserService {
    void saveUser() {}
}

class EmailService {
    void sendEmail() {}
}

O - Open Closed Principle

Classes should be open for extension but closed for modification.

Example:

interface Notification {
    void send();
}

Now we can add EmailNotification, SMSNotification, PushNotification without changing existing code.

L - Liskov Substitution Principle

Child classes should be replaceable with parent classes.

I - Interface Segregation Principle

Do not force classes to implement methods they don't need.

D - Dependency Inversion Principle

Depend on abstractions, not concrete implementations.

class OrderService {

    private Payment payment;

    public OrderService(Payment payment) {
        this.payment = payment;
    }
}

How to Approach Any LLD Problem

Whenever an interviewer gives an LLD problem:

Step 1: Understand Requirements

Example:

Design a Parking Lot System.

Questions:

Multiple floors?
Different vehicle types?
Ticket generation?
Payment support?

Step 2: Identify Entities

For Parking Lot:

Vehicle
ParkingSpot
Ticket
Floor
Payment

Step 3: Define Relationships

Vehicle -> Ticket

Ticket -> Parking Spot

Parking Lot -> Floors

Floor -> Parking Spots

Step 4: Create Class Diagram

Draw classes before coding.

This avoids unnecessary refactoring later.

Step 5: Apply Design Patterns

Common patterns used in LLD:

Singleton
Factory
Strategy
Observer
Builder

Why LLD Matters

LLD helps developers:

Write clean code
Build scalable applications
Improve maintainability
Perform better in interviews
Design enterprise-level systems

Companies often evaluate LLD skills for Software Engineer II, Senior Engineer, and Architect roles.

Conclusion

Low-Level Design is not about memorizing design patterns. It is about creating software that is easy to understand, extend, and maintain.

To master LLD:

Learn OOP thoroughly.
Understand SOLID principles.
Practice design patterns.
Solve real-world design problems.
Implement systems in Java.

In upcoming blogs, we will design complete systems such as:

Parking Lot System
Library Management System
ATM System
BookMyShow
Splitwise
Elevator System

Stay tuned for more Low-Level Design examples using Java.

DEV Community: Pavan Barnana

RAG (Retrieval-Augmented Generation) Explained for Beginners: Build AI Applications Using Your Own Data

Introduction

What is RAG?

Simple Example

Why Do We Need RAG?

1. Outdated Knowledge

2. Hallucinations

3. No Access to Private Data

4. Expensive Fine-Tuning

How RAG Works

Phase 1: Data Preparation

Step 1: Collect Data

Step 2: Text Extraction

Step 3: Chunking

Step 4: Generate Embeddings

Step 5: Store in Vector Database

Query Processing Phase

Step 1: Convert Question to Embedding

Step 2: Similarity Search

Step 3: Send Context to LLM

Step 4: Generate Final Answer

RAG Architecture

Key Components of RAG

1. Data Sources

2. Embedding Model

3. Vector Database

4. Retriever

5. LLM

Advantages of RAG

More Accurate Answers

Reduced Hallucinations

Real-Time Updates

Lower Cost

Enterprise Friendly

Real-World Use Cases

Enterprise Knowledge Assistant

Customer Support Chatbots

Legal Document Search

Healthcare Assistants

Educational Platforms

Tech Stack for Building a RAG Application

Conclusion

Low-Level System Design (LLD) in Java: A Beginner's Guide

Introduction

What is Low-Level Design?

Example

Core Object-Oriented Principles

1. Encapsulation

2. Abstraction

3. Inheritance

4. Polymorphism

SOLID Principles

S - Single Responsibility Principle

O - Open Closed Principle

L - Liskov Substitution Principle

I - Interface Segregation Principle

D - Dependency Inversion Principle

How to Approach Any LLD Problem

Step 1: Understand Requirements

Step 2: Identify Entities

Step 3: Define Relationships

Step 4: Create Class Diagram

Step 5: Apply Design Patterns

Why LLD Matters

Conclusion