How to Build a Strong RAG Agent (Step-by-Step Guide) by David Malick Dieng

Introduction

Large Language Models (LLMs) are powerful, but they come with a well-known limitation: hallucinations — confidently making things up.

That’s where Retrieval-Augmented Generation (RAG) comes in. By connecting an LLM to an external knowledge base, we can ground its answers in real data.

In this article, I’ll show you how to build a strong RAG agent from scratch, explain the key components, and share best practices to make it production-ready. By the end, you’ll have a working pipeline and a roadmap to scale it into multi-agent systems.

What is RAG?

RAG = Retriever + Generator

Retriever: Finds the most relevant chunks of information from a knowledge base (e.g., vector database).

Generator: Uses the LLM to generate an answer, using both the query + retrieved context.

Without RAG:

Q: “When was OpenAI founded?”
A: “In the 1980s by Steve Jobs.” (🤦 hallucination)

With RAG:

Q: “When was OpenAI founded?”
A: “OpenAI was founded in December 2015 by Sam Altman, Elon Musk, and others.”

📌 RAG ensures factual accuracy by grounding LLMs in external knowledge.

🛠️ Core Components of a Strong RAG Agent

To make your RAG agent robust, you need to get these pieces right:

Chunking → Split documents into meaningful, overlapping chunks (too big = missed context, too small = fragmented info).

Embeddings → Convert chunks into vector representations using models like OpenAI text-embedding-3-large or open-source all-MiniLM-L6-v2.

Vector Database → Store embeddings for fast semantic search (Pinecone, Weaviate, FAISS, Milvus).

Retriever → Finds top-k relevant chunks.

Generator (LLM) → Produces the final answer (OpenAI GPT-4, Claude, or LLaMA).

Orchestration → Frameworks like LangChain or LlamaIndex to connect it all.

Step-by-Step Implementation

We’ll build a minimal RAG pipeline using LangChain + FAISS.

pip install langchain openai faiss-cpu tiktoken

`from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

1. Load documents (example text)

docs = [
"OpenAI was founded in December 2015 by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, and Wojciech Zaremba.",
"RAG stands for Retrieval-Augmented Generation. It combines external knowledge with LLMs."
]

2. Split documents into chunks

splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
documents = splitter.create_documents(docs)

3. Create embeddings + store in FAISS

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

4. Create retriever

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

5. Build RAG pipeline (Retriever + Generator)

qa = RetrievalQA.from_chain_type(
llm=OpenAI(model="gpt-3.5-turbo"),
retriever=retriever
)

6. Query the RAG agent

query = "Who founded OpenAI?"
result = qa.run(query)
print(result)
`

This simple RAG agent retrieves relevant info and feeds it to GPT for accurate answers.

Best Practices for a “Strong” RAG Agent

Optimize chunk size (200–500 tokens with 10–20% overlap).

Hybrid search → Combine semantic + keyword search for better recall.

Metadata filtering → Tag docs with source, date, etc., and filter by context.

Evaluate regularly → Use frameworks like LangSmith
to measure hallucinations & accuracy.

Cache results for repeated queries (e.g., Redis).

🤖 Multi-Agent RAG Collaboration

A single RAG agent is powerful, but the future is multi-agent systems:

Research Agent → Finds data.

Summarizer Agent → Compresses info.

QA Agent → Delivers the final polished answer.

Together, they act like a team of specialists, each grounded in the same RAG pipeline.

Example use case:
📚 AI Tutors → one agent finds knowledge, another explains it, another checks correctness.

📂 Resources & Next Steps

🔗 LangChain Docs

🔗 LlamaIndex

🔗 Awesome RAG GitHub