Introduction to RAG Architecture

30 min

Retrieval-Augmented Generation (RAG)

RAG is one of the most powerful patterns for building AI applications. It combines the reasoning capabilities of LLMs with the accuracy of your own data sources.

**Why RAG?** - LLMs have knowledge cutoffs (GPT-4: April 2023) - LLMs can hallucinate facts - You need answers from YOUR data - RAG grounds responses in real documents

RAG Architecture Overview

ℹ️RAG = Retrieve relevant documents → Augment the prompt with them → Generate a grounded response

pythonThe three-step RAG pattern

# Basic RAG Flow
from openai import OpenAI
from vectordb import VectorStore

def rag_query(user_question: str) -> str:
    # 1. RETRIEVE: Find relevant documents
    docs = vector_store.search(
        query=user_question,
        top_k=5  # Get top 5 relevant chunks
    )
    
    # 2. AUGMENT: Build context from documents
    context = "\n\n".join([doc.text for doc in docs])
    
    # 3. GENERATE: Ask LLM with context
    prompt = f"""Answer based on the following context:
    
Context:
{context}

Question: {user_question}

Answer:"""
    
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.choices[0].message.content

RAG Fundamentals

Question 1 of 2

What problem does RAG primarily solve?

NextUnderstanding Embeddings