LLM Integration: How to Make Your Company Data Talk

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.

What Is the RAG Architecture?

RAG consists of three core steps:

1. Indexing — Your documents are split into chunks and converted into embeddings
2. Retrieval — The most relevant chunks for the user's question are pulled from a vector DB
3. Generation — The retrieved context is sent to the LLM along with the prompt

Technical Implementation

A typical RAG pipeline in Python:

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import ChatOpenAI

# 1. Vectorize documents
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)

# 2. Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# 3. Create QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4"),
    retriever=retriever,
    return_source_documents=True
)

# 4. Ask a question
result = qa_chain.invoke({"query": "What is our return policy?"})

Performance Metrics

Metrics we measured after RAG integration:

• Correct answer rate: 92% (previously 45% hallucination)
• Average response time: 2.3 seconds
• User satisfaction: 87%

Conclusion

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.