What is RAG? Retrieval-Augmented Generation Explained Simply (2026)

SynfraCore·January 2026·8 min read

The Problem RAG Solves

Large Language Models like GPT-4 and Claude/SynfraAI know a lot — but they were trained on data up to a certain date, and they do not know anything specific to your organisation: internal documents, customer data, recent updates, proprietary knowledge. RAG (Retrieval-Augmented Generation) solves this by giving the LLM relevant documents at query time.

How RAG Works

1. INGEST (done once):
   Your documents → split into chunks → convert to vectors (embeddings)
   → store in a vector database

2. QUERY (every user question):
   Question → converted to vector → vector DB finds similar chunks
   → top K chunks + question sent to LLM → answer with context

Build a Simple RAG System

python

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
import anthropic

# Step 1: Load and split
docs = PyPDFLoader('handbook.pdf').load()
chunks = RecursiveCharacterTextSplitter(chunk_size=500).split_documents(docs)

# Step 2: Create vector store
vectorstore = Chroma.from_documents(chunks, HuggingFaceEmbeddings())

# Step 3: Answer questions
def ask(question):
    results = vectorstore.similarity_search(question, k=3)
    context = '\n\n'.join([r.page_content for r in results])
    client = anthropic.Anthropic()
    msg = client.messages.create(
        model='claude-sonnet-4-6', max_tokens=1024,
        messages=[{'role': 'user', 'content': f'Context:\n{context}\n\nAnswer: {question}'}]
    )
    return msg.content[0].text

RAG vs Fine-tuning

Fine-tuning: Train model on your data — expensive, slow to update, good for style/format adaptation.

RAG: Retrieve context at query time — cheap, always up to date, good for factual Q&A over documents.

For most enterprise use cases in 2026, RAG is the correct approach. Fine-tune only when RAG cannot solve the problem.

Learn More

See the AI Academy RAG section for hands-on RAG tutorials with code examples.

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