What is RAG? Retrieval-Augmented Generation Explained

#ai #llm #rag #tutorial

Originally published at chudi.dev

TL;DR

RAG (Retrieval-Augmented Generation) combines language models with real-time data retrieval to provide accurate, up-to-date responses. Key benefit: Reduces hallucination by grounding responses in actual documents.

What is RAG?

RAG is a technique that gives LLMs access to external knowledge at inference time. Instead of relying solely on what the model learned during training--which could be months or years old--RAG pulls in relevant documents before generating a response.

Without me realizing it, I had been using a form of RAG every time I asked Claude to help me understand a codebase. Feeding it context before asking questions? That's the RAG pattern in action.

How RAG Works

Query Processing: User question is received
Retrieval: Relevant documents are fetched from a knowledge base
Augmentation: Retrieved context is added to the prompt
Generation: LLM generates a response using both its training and the retrieved context

I thought RAG was only for enterprise systems. Well, it's more like... the pattern exists everywhere we add context to AI conversations.

Why This Matters for Builders

I hated the feeling of asking an AI a question and getting confidently wrong information. But I love being able to trust responses when they're grounded in actual sources.

That specific relief of knowing where information comes from--it changes how you build with AI entirely.

Common RAG Use Cases

Getting Started with RAG

The simplest RAG implementation:

from langchain import OpenAI, VectorStore

# 1. Load and embed your documents
documents = load_documents("./docs")
vectorstore = VectorStore.from_documents(documents)

# 2. Retrieve relevant context
query = "How do I authenticate users?"
context = vectorstore.similarity_search(query, k=3)

# 3. Generate with context
response = llm.generate(
    prompt=f"Context: {context}\n\nQuestion: {query}"
)

FAQ Section

See the FAQ schema above for common questions about RAG.

Since I no longer need to second-guess every AI response, I can focus on what I actually want to build. I like to see it as a comparative advantage--understanding RAG means building more reliable AI applications.