Guide to get started with Retrieval-Augmented Generation (RAG)

🔹 What is RAG? (in simple words)

Retrieval-Augmented Generation (RAG) combines:

• Search (Retrieval) → find relevant information from your data
• Generation → let an LLM generate answers using that data

👉 Instead of guessing, the AI looks up facts first, then answers.

🧠 Why RAG is important

• Reduces hallucinations
• Answers from your own data (PDFs, docs, DBs, APIs)
• Keeps data up-to-date (no retraining needed)
• Perfect for chatbots, internal tools, search, Q&A

🧩 RAG Architecture (high level)

Flow:

1. User asks a question
2. Relevant documents are retrieved
3. Retrieved context is sent to LLM
4. LLM generates an answer grounded in data

🛠️ Core Components of RAG

1️⃣ Data Source

• PDFs
• Word files
• Markdown
• Databases
• APIs
• Websites

2️⃣ Embeddings

Text → numerical vectors for similarity search
Popular models:

• OpenAI embeddings
• SentenceTransformers

3️⃣ Vector Database

Stores embeddings for fast search:

• FAISS (local)
• Pinecone
• Weaviate
• Chroma

4️⃣ LLM (Generator)

Examples:

• GPT-4 / GPT-4o
• Claude
• Llama

⚙️ Minimal RAG Setup (Beginner)

Step 1: Install dependencies

pip install langchain faiss-cpu openai tiktoken

Step 2: Load & embed documents

from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

loader = TextLoader("data.txt")
docs = loader.load()

embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)

Step 3: Retrieve + generate answer

query = "What is RAG?"
docs = db.similarity_search(query)

from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI()

response = llm.predict(
    f"Answer using this context:n{docs}nnQuestion:{query}"
)

print(response)

🎉 That’s a working RAG system

🧪 What RAG is used for (real examples)

• 📄 PDF Chatbots
• 🏢 Internal company knowledge base
• 🧑‍⚖️ Legal document search
• 🩺 Medical guidelines assistant
• 💻 Developer documentation bots

⚠️ Common beginner mistakes

❌ Stuffing too much text into prompt
❌ Not chunking documents
❌ Using wrong chunk size
❌ Skipping metadata
❌ Expecting RAG to “reason” without good data

✅ Best practices (Day-1)

• Chunk size: 500–1000 tokens
• Add source citations
• Use top-k retrieval (k=3–5)
• Keep prompts explicit: “Answer only from context”

🚀 Next steps (recommended)

• Add document chunking
• Use metadata filtering
• Add citations
• Use hybrid search (keyword + vector)
• Add reranking

🧠 When NOT to use RAG

• Math-heavy reasoning
• Code generation without context
• Creative writing
• Pure chatbots

AI with graphs 15 april conf

https://neo4j.registration.goldcast.io/events/d11441d0-5a74-463d-ab1d-22f03c939c3c
https://sessionize.com/nodesai2026/