
Large language models (LLMs) are excellent at general knowledge but know nothing about your company. RAG architecture solves this: instead of "teaching" the model your documents, you inject search results into the prompt to produce context-aware answers.
RAG consists of three core steps:
Unlike fine-tuning, RAG doesn't modify the model. It only adds context to the prompt — a major advantage for cost and ongoing updates.
A typical RAG pipeline in Python:
from langchain.embeddings import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.chains import RetrievalQAfrom langchain.llms import ChatOpenAI# 1. Vectorize documentsembeddings = OpenAIEmbeddings()vectorstore = Chroma.from_documents(documents, embeddings)# 2. Create retrieverretriever = vectorstore.as_retriever(search_kwargs={"k": 5})# 3. Create QA chainqa_chain = RetrievalQA.from_chain_type(llm=ChatOpenAI(model="gpt-4"),retriever=retriever,return_source_documents=True)# 4. Ask a questionresult = qa_chain.invoke({"query": "What is our return policy?"})
Metrics we measured after RAG integration:
RAG is the most practical and cost-effective way to run LLMs over your proprietary data. It deploys far faster than fine-tuning, and when documents change you only need to refresh the index.