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

Source: Dev.to

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, which reduces hallucinations.

Benefits

• Answers come from your own data (PDFs, docs, DBs, APIs)
• Keeps data up‑to‑date (no retraining needed)
• Ideal for chatbots, internal tools, search, and Q&A

RAG Architecture (high level)

Flow

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

Supported source types

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

Technical pipeline

• Text → numerical vectors for similarity search
• Embeddings: OpenAI embeddings, SentenceTransformers
• Vector stores for fast search: FAISS (local), Pinecone, Weaviate, Chroma

Model examples

• GPT‑4 / GPT‑4o
• Claude
• Llama

Example implementation (Python)

# Install required packages
# pip install langchain faiss-cpu openai tiktoken

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

# Load documents
loader = TextLoader("data.txt")
docs = loader.load()

# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)

# Retrieve relevant chunks
query = "What is RAG?"
retrieved_docs = db.similarity_search(query)

# Generate answer using the retrieved context
llm = ChatOpenAI()
response = llm.predict(
    f"Answer using this context:\n{retrieved_docs}\n\nQuestion: {query}"
)

print(response)

Typical use cases

• PDF chatbots
• Internal company knowledge bases
• Legal document search
• Medical guidelines assistants
• Developer documentation bots

Best practices

• Avoid stuffing too much text into the prompt (chunk size: 500–1000 tokens)
• Add source citations
• Use top‑k retrieval (k = 3–5)
• Keep prompts explicit, e.g., “Answer only from context”

Next steps (recommended)

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

When NOT to use RAG

• Math‑heavy reasoning
• Code generation without context
• Creative writing
• Pure chatbots
• AI that relies on graph data (see Neo4j conference)

References

• Neo4j conference:
• Nodes AI 2026 session: