How to Build a RAG Pipeline

Retrieval augmented generation, or RAG, is a simple idea with a huge practical payoff: instead of asking a model to answer from training data alone, you retrieve relevant context first and send that context along with the prompt.

That makes answers:

• more grounded
• easier to update
• less dependent on model memory

The basic RAG architecture

A typical RAG pipeline has four stages:

1. load documents
2. split them into chunks
3. embed and store the chunks
4. retrieve the most relevant chunks at query time

After retrieval, you place the selected context into the model prompt and ask for the answer.

Why chunking matters

Chunking is one of the biggest quality levers in RAG.

If chunks are too large:

• retrieval becomes noisy
• prompts become expensive
• answers may contain irrelevant context

If chunks are too small:

• important context gets split apart
• the retriever may miss the bigger idea

Good chunking usually balances semantic coherence with token efficiency.

A minimal Python example

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import FAISS

docs = [
    "Trackly helps teams track token usage, cost, and latency across LLM calls.",
    "RAG systems retrieve relevant context before asking the model to answer.",
]

splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=40)
chunks = splitter.create_documents(docs)

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(chunks, embeddings)

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
context_docs = retriever.invoke("How does Trackly help with LLM costs?")

context = "\n\n".join(doc.page_content for doc in context_docs)

llm = ChatOpenAI(model="gpt-4o-mini")
answer = llm.invoke(
    f"Answer the question using only this context:\n\n{context}\n\nQuestion: How does Trackly help with LLM costs?"
)

print(answer.content)

This example is intentionally small, but it captures the entire pattern.

What gets stored in the vector database

Each chunk usually stores:

• the chunk text
• its embedding vector
• metadata such as source file, section, product area, or timestamp

Metadata matters because it lets you filter retrieval later. For example, you might only want:

• docs from a specific product
• articles updated after a date
• content for one customer workspace

Prompt structure matters too

Retrieval alone does not guarantee a good answer. Your generation prompt still needs to be clear.

A common template is:

You are a helpful assistant.
Use only the supplied context.
If the answer is not in the context, say you do not know.

Context:
{retrieved_context}

Question:
{user_question}

This small instruction often reduces hallucinations more than people expect.

Common failure modes

If your first RAG pipeline feels weak, the issue is usually one of these:

• low-quality chunking
• poor embeddings for the task
• weak retrieval settings
• prompts that do not constrain the answer
• missing evaluation

RAG is not just "add a vector database and done." The retrieval step is a product surface that needs tuning.

A practical evaluation loop

Start with 20 to 30 real questions and check:

• did retrieval return the right chunks?
• was the answer grounded in those chunks?
• was the answer concise and useful?
• what kind of questions consistently failed?

This is how you learn whether the issue is retrieval, prompting, or source data.

When a basic pipeline is enough

You do not need advanced RAG for every use case.

Basic RAG is often enough for:

• product docs assistants
• internal policy search
• support knowledge bases
• FAQ copilots

Get the basics working first. Only add reranking, query rewriting, or agentic behavior after you know what is actually broken.

Final takeaway

RAG is powerful because it turns a model from a memory guesser into a system that can answer from fresh, relevant information. Build the smallest pipeline that works, measure retrieval quality early, and treat chunking plus prompt design as first-class parts of the system.