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Neweraofcoding

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

#beginners #llm #rag #tutorial

🔹 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)

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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
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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)
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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}\n\nQuestion:{query}"
)

print(response)
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🎉 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/

-RAG it Till You Make It: Building Smarter AI with Graphs & Gemini 3

  • From the AI Jungle to Structured RAG for AI-Assisted Code Review

RAG- an AI method that boosts Large Language Models (LLMs) by fetching up-to-date, external data to provide more accurate and specific answers, preventing hallucinations
Google Gemini File Search for RAG

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