RAG 101: Designing Retrieval-Augmented Generation Pipelines

Large Language Models are impressive—until they hallucinate.
Retrieval-Augmented Generation (RAG) fixes that by injecting grounded context straight into the prompt.

RAG = Search (retrieval) + Reasoning (generation).

In this post you'll learn:

1. The building blocks of a RAG pipeline
2. Design patterns & trade-offs
3. A < 50-line "hello RAG" demo in Python

---

1. Why Retrieval + Generation?

Challenge: Hallucination
Pure LLM: High
RAG: Grounded answers

Challenge: Domain Freshness
Pure LLM: Fine-tune needed
RAG: Update docs instantly

Challenge: Cost/Latency
Pure LLM: Long prompts
RAG: Retrieve only what's needed

---

Large Language Models are impressive—until they hallucinate.
Retrieval-Augmented Generation (RAG) fixes that by injecting grounded context straight into the prompt.

RAG = Search (retrieval) + Reasoning (generation).

In this post you'll learn:

1. The building blocks of a RAG pipeline
2. Design patterns & trade-offs
3. A < 50-line "hello RAG" demo in Python

---

1. Why Retrieval + Generation?

Challenge: Hallucination
Pure LLM: High
RAG: Grounded answers

Challenge: Domain Freshness
Pure LLM: Fine-tune needed
RAG: Update docs instantly

Challenge: Cost/Latency
Pure LLM: Long prompts
RAG: Retrieve only what's needed

---

2. RAG Architecture at a Glance

┌──────┐  query   ┌──────────┐  top-k  ┌─────┐
│ User │ ───────► │ Retriever│ ──────► │ LLM │
└──────┘          └──────────┘         └─────┘
                         ▲
                         │ embeds
                    ┌───────────┐
                    │ Vector DB │
                    └───────────┘

Components:

• Vector DB: FAISS, Weaviate, PGVector
• Retriever: Similarity, hybrid BM25 + embeddings
• LLM: GPT-4o, Claude, Llama 3

---

3. Design Choices & Trade-Offs

Embeddings
Options: OpenAI, e5-large, Instructor
Trade-off: Quality vs cost

Chunking
Options: Fixed / heading-aware
Trade-off: Recall vs context size

k (Retrieval Count)
Range: 3–10
Trade-off: Higher recall ⇒ higher latency & cost

Prompt Style
Options: stuff / refine / map-reduce
Trade-off: Simplicity vs reasoning depth

---

4. Minimal "Hello RAG"

# Import required libraries
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

# 1. Load documents
docs = TextLoader("docs/").load()

# 2. Create vector store and retriever
store = FAISS.from_documents(docs, OpenAIEmbeddings())
retriever = store.as_retriever(search_kwargs={"k": 4})

# 3. Set up RAG chain
rag = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model_name="gpt-4o"),
    chain_type="stuff",
    retriever=retriever
)

# 4. Query the system
print(rag.run("What's our refund policy?"))

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5. When Not to Use RAG

• Knowledge rarely changes and fine-tuning is cheap
• Answers must be deterministic (e.g., legal citations)
• Latency budgets < 200 ms with no caching wiggle room

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Next Article

→ RAG in Practice: Implementing & Tuning a Production Pipeline
Learn about embeddings at scale, evaluation, and a FastAPI reference pipeline

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