Building a Simple RAG Application

Learning Objectives

By the end of this session, you will be able to:

  • Understand the complete flow of a RAG application

  • Combine embeddings, vector databases, and LLMs into one system

  • Learn the step-by-step RAG implementation process

  • Build a simple knowledge assistant architecture

  • Understand how retrieval and generation work together

  • Explore practical implementation patterns

  • Prepare for building real-world RAG projects

Introduction

So far in this series, we have learned:

Generative AI Foundations

  • LLMs

  • Tokens

  • Embeddings

  • Prompt Engineering

RAG Fundamentals

  • RAG Architecture

  • Data Ingestion

  • Chunking

  • Retrieval

Vector Databases

  • ChromaDB

  • Pinecone

  • Weaviate

  • Similarity Search

Now it is time to bring everything together.

Many students learn these topics individually but struggle to understand how they work as a complete system.

This session closes that gap.

By the end of this lesson, you will understand how to build a complete RAG application from scratch.

Why This Topic Matters

Consider a university chatbot.

Students ask questions such as:

What is the MCA admission deadline?

The answer exists in university documents.

A traditional LLM may:

  • Guess

  • Hallucinate

  • Provide outdated information

A RAG application:

Finds Relevant Document
          ?
Provides Context
          ?
Generates Accurate Answer

This is why RAG has become the foundation of modern AI assistants.

What Are We Building?

We will build a simple knowledge assistant.

Knowledge Source:

University Documents

User Question:

What is the scholarship eligibility criteria?

System:

Retrieve Information
          ?
Generate Answer

This represents the core architecture used in most RAG systems.

High-Level Architecture

Documents
      ?
Chunking
      ?
Embeddings
      ?
Vector Database

User Question
      ?
Embedding
      ?
Similarity Search
      ?
Relevant Chunks
      ?
LLM
      ?
Answer

Every production RAG application follows a similar pattern.

Understanding the Complete Workflow

The workflow can be divided into two major phases.

Phase 1: Knowledge Preparation

Preparing documents for retrieval.

Phase 2: Question Answering

Using the prepared knowledge to answer user questions.

Let's explore both.

Phase 1 – Knowledge Preparation

Before users can ask questions, the system must prepare knowledge.

Example documents:

Admission Guide
Scholarship Policy
Academic Calendar

These documents become the knowledge base.

Step 1 – Collect Documents

The first step is gathering information.

Sources may include:

PDFs

University Handbook.pdf

Websites

University Portal

Databases

Student Information System

Internal Documents

Academic Policies

Everything starts with knowledge collection.

Step 2 – Extract Text

Documents contain:

  • Formatting

  • Images

  • Headers

  • Footers

The system extracts useful text.

Example:

Before:

University Logo
Page Number
Scholarship Guidelines
Footer

After:

Scholarship Guidelines

This simplifies processing.

Step 3 – Chunk Documents

Large documents are divided into smaller chunks.

Example:

100-Page Handbook

becomes:

Chunk 1
Chunk 2
Chunk 3

Chunking improves retrieval precision.

Step 4 – Generate Embeddings

Each chunk is converted into a vector.

Example:

Scholarship Eligibility Requirements

becomes:

[0.34, 0.82, -0.15, ...]

The vector captures semantic meaning.

Step 5 – Store in Vector Database

Embeddings are stored in a vector database.

Example:

Chunk
+
Embedding
+
Metadata

Stored in:

ChromaDB
Pinecone
Weaviate
Qdrant

The knowledge base is now searchable.

Knowledge Preparation Complete

After preparation:

Documents
      ?
Processed
      ?
Embedded
      ?
Stored

The system is ready to answer questions.

Phase 2 – Question Answering

Now users can interact with the system.

This is where retrieval begins.

Step 6 – User Asks a Question

Example:

What scholarships are available for MCA students?

The application receives the query.

Step 7 – Generate Query Embedding

The question is converted into an embedding.

Workflow:

Question
      ?
Embedding Model
      ?
Query Vector

Now the question and documents exist in the same vector space.

Step 8 – Perform Similarity Search

The vector database compares:

Query Vector

against

Document Vectors

Goal:

Find Most Relevant Chunks

Example results:

Scholarship Policy
Financial Aid Guidelines
Student Support Programs

These chunks become the context.

Step 9 – Build Context

Retrieved chunks are combined.

Example:

Chunk A
+
Chunk B
+
Chunk C

The system constructs a prompt.

Example:

Context:
Scholarships are available for MCA students with a minimum 75% score.

Question:
What scholarships are available for MCA students?

The LLM now has supporting information.

Step 10 – Generate Answer

The prompt is sent to the LLM.

Workflow:

Question
      +
Context
      ?
LLM
      ?
Answer

Generated response:

MCA students with a minimum 75% score are eligible for university scholarship programs.

This answer is grounded in retrieved information.

End-to-End RAG Flow

Documents
      ?
Chunking
      ?
Embeddings
      ?
Vector Database

Question
      ?
Embedding
      ?
Similarity Search
      ?
Retrieved Chunks
      ?
LLM
      ?
Answer

This is the complete RAG workflow.

Building a Simple RAG Application in Python

A simplified implementation:

documents = load_documents()

chunks = chunk_documents(documents)

embeddings = create_embeddings(chunks)

store_in_vector_database(embeddings)

question = get_user_question()

query_embedding = create_embedding(question)

results = search_similar_chunks(query_embedding)

context = build_context(results)

answer = generate_response(context, question)

print(answer)

This example shows the overall flow without focusing on framework-specific details.

Building a Simple RAG Application in .NET

A simplified workflow:

var documents = LoadDocuments();

var chunks = ChunkDocuments(documents);

var embeddings = GenerateEmbeddings(chunks);

StoreEmbeddings(embeddings);

var queryEmbedding = GenerateEmbedding(userQuestion);

var results = SearchSimilarDocuments(queryEmbedding);

var answer = GenerateAnswer(results, userQuestion);

Many enterprise .NET solutions follow this architecture.

Real-World Example: University Assistant

Knowledge Base:

Admission Rules
Scholarship Policies
Course Catalog

Question:

What is the MCA admission process?

Workflow:

Search Documents
      ?
Retrieve Admission Policy
      ?
Generate Answer

Students receive accurate information.

Real-World Example: HR Assistant

Knowledge Base:

Leave Policies
Benefits Guide
Travel Rules

Employee asks:

How many annual leave days do I receive?

Workflow:

Retrieve Leave Policy
      ?
Generate Answer

The answer comes from company documents.

Real-World Example: Customer Support

Knowledge Base:

Product Manuals
Troubleshooting Guides
FAQs

Customer asks:

How do I reset my router?

Workflow:

Retrieve Product Instructions
      ?
Generate Step-by-Step Answer

This improves support efficiency.

Why RAG Is Better Than Direct LLM Usage

Traditional LLM

Question
      ?
Memory
      ?
Answer

Problems:

  • Outdated information

  • Hallucinations

  • No private knowledge

RAG

Question
      ?
Knowledge Retrieval
      ?
Context
      ?
Answer

Benefits:

  • Current information

  • Organization-specific knowledge

  • Reduced hallucinations

Common Components in Production Systems

A production RAG application usually includes:

Document Loader

Reads files.

Chunking Engine

Splits content.

Embedding Model

Generates vectors.

Vector Database

Stores embeddings.

Retriever

Finds relevant chunks.

LLM

Generates answers.

Monitoring Layer

Tracks quality and usage.

Production Architecture

Document Sources
       ?
Ingestion Pipeline
       ?
Embeddings
       ?
Vector Database
       ?
Retriever
       ?
LLM
       ?
User Interface

This architecture is used by many enterprise AI assistants.

Common Beginner Mistakes

Poor Chunking

Reduces retrieval quality.

Weak Embedding Models

Produces poor semantic search.

Retrieving Too Much Context

Creates noisy prompts.

Retrieving Too Little Context

Misses important information.

Ignoring Metadata

Limits filtering capabilities.

Understanding these mistakes helps improve system quality.

Measuring Success

A good RAG application should provide:

Relevant Retrieval

Correct documents found.

Accurate Answers

Grounded in evidence.

Fast Response Times

Good user experience.

Reliable Performance

Consistent results.

These metrics are important in production systems.

Enterprise Benefits

Organizations adopt RAG because it enables:

  • Knowledge assistants

  • Internal search platforms

  • Customer support systems

  • Research assistants

  • Document intelligence solutions

RAG is now one of the most important AI application patterns.

.NET Perspective

Common technologies include:

  • Semantic Kernel

  • Azure OpenAI

  • Azure AI Search

  • ASP.NET Core

Many enterprise .NET applications implement RAG using these tools.

Python Perspective

Popular frameworks include:

  • LangChain

  • LlamaIndex

  • ChromaDB

  • Pinecone

  • Weaviate

  • OpenAI SDK

Python remains the most common ecosystem for RAG development.

Assignment

Design Exercise

Design a simple RAG application for:

University Knowledge Assistant

Include:

  • Data sources

  • Embedding model

  • Vector database

  • LLM

  • User interface

Practical Activity

Create a flow diagram showing:

Document
      ?
Chunking
      ?
Embeddings
      ?
Vector Database
      ?
Retrieval
      ?
LLM
      ?
Answer

Explain each step.

Key Takeaways

  • A RAG application combines retrieval and generation.

  • Documents must be processed, chunked, embedded, and stored before retrieval.

  • User questions are converted into embeddings and matched against stored vectors.

  • Retrieved content provides context for the LLM.

  • RAG systems reduce hallucinations and improve answer accuracy.

  • The same architecture powers many modern AI assistants.

  • Understanding the complete workflow is essential for building production RAG systems.

What's Next?

In Session 28, we will build one of the most popular RAG projects:

PDF Question Answering System

You will learn how to upload PDF documents, extract text, create embeddings, perform retrieval, and build an AI assistant capable of answering questions directly from PDF files.