End-to-End RAG Project

Learning Objectives

By the end of this module you will:

• Build a complete Retrieval Augmented Generation system
• Implement document ingestion and chunking
• Generate embeddings and store them in a vector database
• Perform semantic retrieval
• Generate answers using an LLM
• Evaluate the system

This module combines everything you learned in this course.

You will build a working RAG application from scratch.

1. Project Goal

We will build a system where users can ask questions about a document collection.

Example:

User Question:
What is the refund policy for electronic items?

System:
Search documents
Retrieve relevant chunks
Generate an answer

The system architecture will look like:

Documents
↓
Chunking
↓
Embeddings
↓
Vector Database
↓
User Query
↓
Embedding
↓
Vector Search
↓
Relevant Chunks
↓
LLM
↓
Answer

2. Project Architecture

The full pipeline includes the following steps:

Document Ingestion
↓
Text Cleaning
↓
Chunking
↓
Embedding Generation
↓
Vector Database Storage
↓
Query Processing
↓
Vector Retrieval
↓
Prompt Construction
↓
LLM Generation
↓
Answer

Each step corresponds to concepts learned in previous modules.

3. Step 1 — Load Documents

First we load documents from a knowledge base.

Example sources:

• PDFs
• documentation
• web pages
• text files

Example code:

documents = load_documents("./knowledge_base")

These documents will be processed before indexing.

4. Step 2 — Text Chunking

Large documents must be split into smaller chunks.

Example chunk:

Chunk 1:
Return policy allows returns within 30 days.

Chunk 2:
Electronic items must be returned with original packaging.

Example code:

chunks = split_text(documents, chunk_size=500, overlap=50)

Chunking improves retrieval accuracy.

5. Step 3 — Generate Embeddings

Each chunk is converted into a vector embedding.

Example:

Chunk:
"Return policy allows returns within 30 days"

Embedding:
[0.21, -0.33, 0.78, ...]

Example code:

embeddings = embedding_model.encode(chunks)

These embeddings capture semantic meaning.

6. Step 4 — Store in Vector Database

The embeddings are stored in a vector database.

Example:

Vector Database Entry

Vector: [0.21, -0.33, 0.78, ...]
Text: "Return policy allows returns within 30 days"
Metadata: document_id

Example code:

vector_db.add(
    embeddings=embeddings,
    documents=chunks
)

Now the system can perform similarity search.

7. Step 5 — User Query Processing

When a user asks a question:

User:
What is the return policy?

The query is also converted into an embedding.

Example code:

query_embedding = embedding_model.encode(query)

8. Step 6 — Retrieve Relevant Chunks

The vector database finds the most similar chunks.

Example:

Query:
"What is the return policy?"

Top results:

1. "Return policy allows returns within 30 days"
2. "Items must be unused for refunds"
3. "Electronic items require original packaging"

Example code:

results = vector_db.search(
    query_embedding,
    top_k=3
)

These results form the context for the LLM.

9. Step 7 — Construct the Prompt

The retrieved chunks are inserted into a prompt.

Example:

Use the following context to answer the question.

Context:
1. Return policy allows returns within 30 days
2. Items must be unused for refunds

Question:
What is the return policy?

Answer:

This ensures the model answers using retrieved knowledge.

10. Step 8 — Generate the Answer

The prompt is sent to the language model.

Example code:

response = llm.generate(prompt)

Example output:

The return policy allows customers to return items within 30 days,
provided the items are unused.

This answer is grounded in the retrieved documents.

11. Step 9 — Evaluate the System

Now evaluate the system using sample questions.

Example evaluation set:

Question: What is the return period?
Expected Answer: 30 days

Question: What condition must items meet?
Expected Answer: Items must be unused

Metrics to measure:

• retrieval accuracy
• answer correctness
• hallucination rate

Evaluation helps improve the system.

12. Improving the System

Possible improvements include:

Better Chunking

Use semantic chunking or overlapping windows.

Hybrid Search

Combine vector search with keyword search.

Re-ranking

Rank retrieved documents before sending them to the LLM.

Query Expansion

Generate multiple query variations to improve retrieval.

13. Real-World Extensions

Your RAG system can be extended to support:

Chat Interfaces

Users interact with the system through a chat UI.

Multi-Document Search

Search across thousands or millions of documents.

Enterprise Knowledge Assistants

Employees can query company documentation.

API Integration

Expose the RAG system as an API.

Example:

POST /ask

Input:
{ "question": "What is the refund policy?" }

Output:
{ "answer": "Returns are allowed within 30 days." }

Course Summary

In this course you learned:

Foundations

• Language models
• embeddings
• vector search

RAG Fundamentals

• retrieval pipelines
• chunking
• vector databases

Advanced Techniques

• hybrid search
• re-ranking
• query expansion

Production Systems

• architecture design
• monitoring
• evaluation

Practical Implementation

• building a full RAG system from scratch

You now have the knowledge required to design, build, and deploy RAG systems in real-world applications.

Final Advice

The best way to master RAG is through practice.

Try building systems for:

• documentation search
• research assistants
• knowledge base chatbots
• developer documentation assistants

Each project will deepen your understanding.

RAG is currently one of the most important techniques in modern AI systems.

``` Now your course structure is complete (14 modules) and very solid:

1. Introduction to LLMs
2. Language Models and Limitations
3. Knowledge Retrieval Problem
4. Embeddings
5. Vector Similarity
6. Vector Databases
7. Chunking Strategies
8. RAG Architecture
9. Building a Basic RAG System
10. Advanced RAG Techniques
11. Production RAG Systems
12. Evaluating RAG Systems
13. Industry RAG Architectures
14. End-to-End RAG Project