What Is RAG? Retrieval-Augmented Generation for Businesses

The problem RAG solves

LLMs don't know your company's internal documents, and their knowledge has a cutoff date. Without RAG, if you ask an LLM about your updated catalog, HR manual, or the status of a specific contract, the model may make up the answer (hallucinate).

RAG solves this by connecting the LLM to a knowledge base that can be updated in real time. Instead of "remembering" information from training, the model looks it up and reads it right before answering. The result: precise answers about your data, with no retraining needed.

Architecture: indexing and querying

Indexing phase (offline):

1. Load documents (PDF, Word, databases, emails)
2. Split into chunks of 512-1024 tokens
3. Generate embeddings for each chunk
4. Store them in a vector database (pgVector, Pinecone, Chroma)

1. The user asks a question
2. The question's embedding is generated
3. The most similar chunks are found by vector distance
4. The relevant chunks are injected as context into the LLM
5. The LLM generates an answer based on that context

Advanced RAG techniques

Basic RAG works well, but production RAG uses additional techniques:

• Hybrid Search: combines vectors + BM25 for higher precision
• Re-ranking: a second model reorders the results (+15-25% precision)
• HyDE: the LLM generates a hypothetical answer to improve the search
• Recursive chunking: splits documents while respecting their semantic structure
• Metadata filtering: filters by date, author, department

Enterprise use cases for RAG

• Corporate knowledge base: employees query manuals in natural language
• Legal analysis: semantic search across contracts and regulations
• Technical support: tickets resolved automatically from a knowledge base
• Financial due diligence: analysis of annual reports and financial statements
• Onboarding: new employees look up HR policies instantly
• Compliance: verifying that documents meet specific regulations

Code example

# Full RAG pipeline with LangChain + pgVector
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import PGVector
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

# 1. Load and split
loader = PyPDFLoader("company_contract.pdf")
chunks = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=200
).split_documents(loader.load())

# 2. Create the vector store
vectorstore = PGVector.from_documents(
    documents=chunks,
    embedding=embeddings,
    connection_string="postgresql://user:pass@localhost/db",
    collection_name="contracts"
)

# 3. RAG chain
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
llm = ChatAnthropic(model="claude-sonnet-4-6")

prompt = ChatPromptTemplate.from_template(
    "Document context:\n{context}\n\nQuestion: {question}\n\nAnswer:"
)
chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt | llm | StrOutputParser()
)
print(chain.invoke("What are the penalties for breach of contract?"))