RAG Architecture: Making LLMs Domain-Expert for Your Business

Why RAG?

Base LLMs are trained on public data up to a cutoff date. They know nothing about your:

• ▸Internal documents and policies
• ▸Real-time data and events
• ▸Proprietary knowledge base

RAG solves this by retrieving relevant context at inference time.

The RAG Pipeline

• •Ingestion: Chunk documents, generate embeddings, store in vector DB
• •Retrieval: At query time, find semantically similar chunks
• •Generation: Feed retrieved context + query to LLM

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.chains import RetrievalQA

vectorstore = Pinecone.from_existing_index("knowledge-base", OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=retriever,
    return_source_documents=True
)

Advanced RAG Techniques

Hybrid search: Combine vector similarity with keyword search (BM25) for better recall. HyDE: Generate a hypothetical answer, embed it, then retrieve — improves retrieval quality by 20-30%. Re-ranking: Use a cross-encoder to re-rank retrieved chunks before passing to the LLM.