RAG: Complete Guide

What is RAG?

🎯 The Problem

Large Language Models (LLMs) like GPT have limitations:

✗ Knowledge Cutoff: They only know information up to their training date
✗ No Private Data: They don't have access to your company's documents
✗ Hallucinations: They may make up facts when they don't know

💡 The Solution: RAG

Retrieval-Augmented Generation solves this by:

✓ Retrieval: Finding relevant documents from your knowledge base
✓ Augmented: Adding those documents to the LLM's context
✓ Generation: LLM generates answer using the provided context

📚 Think of it like an Open-Book Exam

Without RAG: The LLM is like a student taking a closed-book exam — can only use what they memorized.
With RAG: The LLM is like a student with access to textbooks — can look up relevant information before answering.

Complete RAG Flow: Start to End

PHASE 1

Offline Indexing (One-Time Setup)

Your Documents

Text Chunking Split into pieces

Embedding Model Text → Vectors

Vector Database Store embeddings

PHASE 2

Online Query (Every User Question)

User Question

Embed Query Same model

Retrieve Top-K Similar docs

LLM + Context Generate answer

Answer

📋 Step-by-Step Breakdown

Document Chunking

Split your documents into smaller, manageable pieces (chunks). This is crucial because:

• LLMs have limited context windows (4K-128K tokens)
• Smaller chunks allow for more precise retrieval
• Typical chunk size: 256-1024 tokens with 10-20% overlap

# Example chunking
chunks = split_text(document, chunk_size=512, overlap=50)
           

Generate Embeddings

Convert each text chunk into a numerical vector (embedding) that captures its semantic meaning:

• Use models like OpenAI's text-embedding-ada-002, Cohere, or open-source alternatives
• Each chunk becomes a vector of 768-1536 dimensions
• Similar texts have similar vectors (closer in vector space)

# Generate embeddings
embeddings = embedding_model.encode(chunks)
# Result: [[0.02, -0.15, 0.89, ...], [...], ...]
           

Store in Vector Database

Save embeddings in a specialized database optimized for similarity search:

• Popular options: Pinecone, Weaviate, Chroma, Qdrant, FAISS
• Stores both the vector AND the original text chunk
• Enables fast nearest-neighbor search

# Store in vector DB
vector_db.upsert([

               {"id": i, "values": emb, "metadata": {"text": chunk}}

               for i, (emb, chunk) in enumerate(zip(embeddings, chunks))

             ])

User Asks a Question

When a user submits a question, the same embedding model converts it to a vector:

user_query = "What are the benefits of RAG?"
query_embedding = embedding_model.encode(user_query)
           

Retrieve Relevant Documents

Find the top-K most similar documents using vector similarity search:

• Uses cosine similarity or dot product to measure closeness
• Typically retrieve 3-10 most relevant chunks
• Returns both similarity scores and original text

# Semantic search
results = vector_db.query(

               vector=query_embedding,

               top_k=5

             )
context = "\n".join([r.metadata["text"] for r in results])
           

Augment Prompt with Context

Combine the user's question with retrieved documents in a prompt:

prompt = f"""
Answer the question based on the context below.
If the answer is not in the context, say "I don't know."

Context:
{context}

Question: {user_query}
Answer:
"""
           

LLM Generates Answer

The LLM reads the context and generates a grounded answer:

response = llm.generate(prompt)
print(response)
# "RAG provides benefits including: access to current
# information, reduced hallucinations, and the ability
# to cite sources for transparency..."
           

Implementation Plan

1 Week 1

Setup & Data Prep

• Choose tech stack
• Set up vector DB
• Collect documents
• Design chunking strategy

2 Week 2

Indexing Pipeline

• Implement chunking
• Set up embedding
• Index all documents
• Test retrieval quality

3 Week 3

Query Pipeline

• Build retrieval logic
• Design prompts
• Integrate LLM
• Add error handling

4 Week 4

Testing & Deploy

• Evaluate accuracy
• Tune parameters
• Build UI/API
• Deploy to production

🛠️ Recommended Tech Stack

📦 Vector Databases

Pinecone Managed

Weaviate Open Source

Chroma Lightweight

🧠 Embedding Models

OpenAI Ada-002 Best

Cohere Embed Great

Sentence-BERT Free

🤖 LLM Options

GPT-4 / GPT-4o Best

Claude 3 Great

Llama 3 Open

💻 Complete Code Example

         # Complete RAG Implementation with LangChain + Chroma
        

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

# Step 1: Load documents
loader = TextLoader("your_documents.txt")
documents = loader.load()

# Step 2: Split into chunks
splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=50

         )
chunks = splitter.split_documents(documents)

# Step 3: Create embeddings & store in vector DB
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

# Step 4: Create retrieval chain
llm = ChatOpenAI(model="gpt-4")
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever(k=5),
return_source_documents=True

         )

# Step 5: Ask questions!
result = qa_chain({"query": "What is RAG?"})
print(result["result"])
       

System Architecture

Data Layer

Documents

PDFs, Docs, Web pages, Databases

Vector DB

Embeddings + metadata storage

Processing Layer

Chunker

Text splitting service

Embedder

Embedding model API

LLM

GPT-4, Claude, Llama

Application Layer

RAG API

REST/GraphQL endpoint

User Interface

Chat UI, Dashboard

💡 Key Insight: The Model Stays Unchanged

RAG doesn't modify or retrain the LLM. Instead, it augments the input with relevant context at inference time. This means:

✓ No Training Needed

Update knowledge by just adding new documents

✓ Always Current

Knowledge can be updated in real-time

✓ Cost Effective

No expensive fine-tuning required

🚀 Ready to Build Your RAG System?

RAG bridges the gap between static LLM knowledge and your dynamic, private data. Start with a simple prototype and iterate from there!

Next Step: Why is RAG powerful? →