You’ve probably heard about AI systems that can answer questions using company documents or personal files. I found myself building exactly this kind of technology—a RAG system—and learned that creating a prototype is one thing, but making it robust enough for daily use is a different challenge. This article shares the practical steps and hard-won insights from building these systems for production.

The goal is simple: connect a user’s question to relevant information and generate a clear, accurate answer. How do we make this process reliable and fast? It starts with preparing your documents.

Think of your documents as a library. Before anyone can find a book, they need to be cataloged and placed on the correct shelf. We do this by “chunking” text into smaller pieces. A simple method splits by a fixed number of characters, but this can cut sentences in half. A better way respects the natural structure of the text.

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50,
    separators=["\n\n", "\n", ". ", " ", ""]
)
chunks = text_splitter.split_text(your_document_text)

This code tries to keep paragraphs and sentences together, making each chunk more coherent. But what if your data is messy, with PDFs, Word docs, and HTML all mixed together?

Once your text is prepared, it needs to be transformed into something a computer can compare. This is where embeddings come in. An embedding is a list of numbers that captures the semantic meaning of a text chunk. “Canine” and “dog” will have similar numbers, even though the words are different. We store these embeddings in a specialized database designed for fast similarity searches.

Two popular choices are ChromaDB, which is great for local development, and Pinecone, a managed service for scaling up. Here’s a basic setup with Chroma:

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings,
    persist_directory="./my_chroma_db"
)
# Now you can search it
results = vectorstore.similarity_search("What is the refund policy?", k=3)

The basic search above finds text that is semantically close to the question. However, what if the best answer requires matching specific keywords, like a product code? Production systems often use a “hybrid” approach. They combine a keyword search (which finds exact matches) with the semantic vector search. The results from both methods are blended and re-sorted to provide the best possible context for the AI.

Now comes the final step: the generation. We take the user’s question and the most relevant text chunks we retrieved, and feed them to a large language model (LLM) like GPT-4. The key is to instruct the model clearly: “Answer the question based only on the following context.” This stops the AI from inventing information.

from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

llm = ChatOpenAI(model="gpt-4", temperature=0)
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)
answer = qa_chain.run("How does the premium warranty work?")

This creates a functional pipeline. But is your system truly ready for hundreds of users? A production-ready system needs more. It needs memory to handle follow-up questions like “Can you tell me more about that?” It needs monitoring to track which questions return empty results or slow down. It also needs a way to update its knowledge when your documents change without rebuilding everything from scratch.

The real test is asking: does the system fail gracefully? If the retrieval finds nothing useful, it should say “I don’t know” instead of guessing. Building this level of robustness is what separates a promising demo from a trusted tool.

Have you considered how you would check if the answers are correct? This is a major focus once the system is running. You might use a framework like RAGAS to score the faithfulness and relevance of answers automatically, creating a feedback loop to improve the system over time.

Moving from a working script to a solid application involves these layers of refinement. The tools are powerful, but they require careful design and an understanding of where things can break. The journey involves constant testing with real questions and being honest about the system’s limitations.

I built my first version in a weekend, but spent months refining it. The effort pays off when you see users getting instant, accurate answers from a mountain of documents they would never have time to read. If you’re starting this journey yourself, focus on a clean data pipeline and a robust retrieval process first. The rest builds from there.

Did this guide help clarify the path to a production RAG system? What part of the architecture are you most curious about? Share your thoughts in the comments below—I’d love to hear what you’re building. If you found this useful, please like and share it with another developer who might be tackling the same challenge