Implementing RAG with Pinecone and LlamaIndex

Retrieval-Augmented Generation (RAG) has become the go-to pattern for building context-aware LLM applications. Today I’ll walk through my production setup.

The Problem

Large Language Models hallucinate. They make up facts with confidence. RAG solves this by grounding responses in your actual data.

Architecture Overview

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores import PineconeVectorStore
import pinecone

pinecone.init(api_key="your-key", environment="us-west1-gcp")
index = pinecone.Index("rag-index")

vector_store = PineconeVectorStore(pinecone_index=index)

documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(
    documents, 
    vector_store=vector_store
)

Key Learnings

1. Chunk size matters - 512 tokens with 50 token overlap works well
2. Metadata filtering - Essential for multi-tenant setups
3. Hybrid search - Combine dense + sparse retrieval for better recall

Performance Numbers

Metric	Before RAG	After RAG
Accuracy	62%	94%
Latency	200ms	450ms
User Trust	Low	High

The latency increase is acceptable given the accuracy gains.