What Is Retrieval-Augmented Generation (RAG)? A Complete Beginner’s Guide

Ever wished ChatGPT or Claude could answer questions using your own documents or website content? That’s exactly what Retrieval-Augmented Generation (RAG) does. It’s like giving your AI assistant access to a custom knowledge base — so it can generate smarter, more relevant answers.

🔍 What is RAG in Simple Terms?

RAG combines two powerful steps:

1. Retrieval: It searches a set of documents (like PDFs, websites, or notes) to find relevant chunks.
2. Generation: It feeds those chunks into an LLM (like Claude or GPT) to generate an accurate, helpful answer.

Think of it like this:
User → “What are the steps for applying for a mortgage?”
RAG → Searches your finance PDFs → Finds a page with the steps → Sends it to Claude → Claude writes a helpful answer based on that info.

📦 What You'll Build

In this tutorial, we’ll create a simple RAG system that:

• 📝 Lets you upload a PDF or TXT file
• 🔍 Searches that file for relevant content
• 🤖 Sends it to Claude to answer user questions

🛠️ Step 1: Set Up Your Environment

mkdir rag_demo
cd rag_demo
python -m venv venv
source venv/bin/activate
pip install streamlit anthropic python-dotenv PyMuPDF

Create a `.env` file and paste in your Claude API key:

ANTHROPIC_API_KEY=your-api-key-here

📄 Step 2: Create the App

Here’s a basic RAG app using Claude + Streamlit:

import streamlit as st
import os
import fitz  # PyMuPDF
from dotenv import load_dotenv
import requests

load_dotenv()
API_KEY = os.getenv("ANTHROPIC_API_KEY")

def extract_chunks(pdf_file):
    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
    chunks = []
    for page in doc:
        text = page.get_text()
        if text.strip():
            chunks.append(text)
    return chunks

def find_relevant_chunks(chunks, query):
    return [chunk for chunk in chunks if query.lower() in chunk.lower()]

def ask_claude(question, context):
    prompt = f"""
You are a helpful assistant. Use the following context to answer the user's question.

Context:
{context}

Question:
{question}
"""
    headers = {
        "x-api-key": API_KEY,
        "anthropic-version": "2023-06-01",
        "Content-Type": "application/json"
    }
    body = {
        "model": "claude-3-haiku-20240307",
        "temperature": 0.5,
        "max_tokens": 800,
        "messages": [{"role": "user", "content": prompt}]
    }
    res = requests.post("https://api.anthropic.com/v1/messages", headers=headers, json=body)
    return res.json()["content"]

st.title("📚 RAG Demo with Claude")
pdf_file = st.file_uploader("Upload a PDF", type="pdf")
query = st.text_input("What do you want to ask?")

if pdf_file and query:
    chunks = extract_chunks(pdf_file)
    relevant = find_relevant_chunks(chunks, query)
    context = "\n---\n".join(relevant[:3])  # Limit to top 3 matches
    answer = ask_claude(query, context)
    st.subheader("Claude's Answer:")
    st.markdown(answer)

🚀 Step 3: Run the App

streamlit run app.py

Try uploading a user manual, policy guide, or your class notes. Then ask things like:

• “What is the return policy?”
• “How do I reset my device?”
• “What are the grading criteria?”

💡 Why RAG Is So Powerful

• 🧠 Gives AI access to current, private, or niche data
• 📚 Reduces hallucination by grounding answers in real info
• 🔐 Keeps sensitive data local (vs training a custom model)

🌱 Want to Go Further?

Upgrade your RAG system by:

• 🔍 Using semantic search (via embeddings) instead of keyword matching
• 🖼️ Including page previews or links to source documents
• 💾 Caching document indexes for faster lookup

✅ Recap

RAG lets you combine your knowledge base with the reasoning power of AI. It’s one of the most practical, powerful ways to build AI apps for real-world use cases — and now you’ve built your first one!

🚨 Coming soon: “Build a Multi-Document RAG System with Embeddings + Claude” — stay tuned!