Portfolio/Learn/Building a Production RAG Pipeline from Scratch
Machine Learning & AIAdvanced

Building a Production RAG Pipeline from Scratch

Step-by-step guide to building a Retrieval-Augmented Generation pipeline with vector embeddings, chunking strategies, and LLM integration.

18 min read
March 5, 2026
RAGLLMEmbeddingsLangChainPython

What is RAG?

Retrieval-Augmented Generation (RAG) combines the power of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG fetches relevant documents at query time and includes them in the prompt context.

RAG reduces hallucinations by grounding LLM responses in actual source documents, making it essential for enterprise AI applications.

Step 1: Document Chunking

The first step is splitting your documents into meaningful chunks. Chunk size matters — too small and you lose context, too large and you dilute relevance. A good starting point is 500-1000 tokens with 100-token overlap.

python
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=800,
    chunk_overlap=100,
    separators=["\n\n", "\n", ". ", " "],
)

chunks = splitter.split_documents(documents)
print(f"Split {len(documents)} docs into {len(chunks)} chunks")

Step 2: Embedding and Indexing

Each chunk gets converted to a dense vector using an embedding model. These vectors are stored in a vector database for fast similarity search at query time.

python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = FAISS.from_documents(chunks, embeddings)

# Save for later use
vectorstore.save_local("./faiss_index")

Step 3: Retrieval and Generation

At query time, embed the user's question, find the top-k most similar chunks, and include them in the LLM prompt. The model then generates a grounded answer using the retrieved context.

python
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA

llm = ChatOpenAI(model="gpt-4o", temperature=0)
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True,
)

result = qa_chain.invoke({"query": "How does chunking affect RAG quality?"})
print(result["result"])

Always evaluate your RAG pipeline with metrics like faithfulness, answer relevancy, and context precision. Tools like RAGAS make this straightforward.

More Articles