Retrieval-Augmented Generation (RAG) is the most practical architecture for building AI applications that need to reference specific knowledge. This tutorial walks through building a production RAG system from scratch.
Architecture Overview
A RAG system has four components: (1) Document ingestion and chunking, (2) Embedding and vector storage, (3) Retrieval, and (4) Generation. Each component has critical design decisions that affect the quality of your final output.
Step 1: Document Chunking
The biggest mistake people make is using naive fixed-size chunking (split every 500 tokens). Instead, use semantic chunking: split documents at natural boundaries (paragraphs, sections, headings) and keep related content together.
LangChain’s RecursiveCharacterTextSplitter with chunk_size=1000 and chunk_overlap=200 is a good starting point. For structured documents (PDFs, HTML), use document structure-aware splitters that respect headings and lists.
Step 2: Embedding Model Selection
For English text, OpenAI’s text-embedding-3-large offers the best quality-to-cost ratio. For multilingual applications, Cohere’s embed-v3 is superior. For on-premise deployment, use sentence-transformers/all-MiniLM-L6-v2 (fast, decent quality) or BAAI/bge-large-en-v1.5 (slower, better quality).
Step 3: Vector Database
For production, choose between: Pinecone (managed, scales well), Weaviate (open source, hybrid search), or pgvector (PostgreSQL extension, simplest if you already use Postgres). For this tutorial, we’ll use pgvector.
Step 4: Retrieval Strategy
Simple vector similarity search is rarely sufficient. Use hybrid search (vector + keyword), re-ranking with a cross-encoder model, and query decomposition for complex questions. Implement these incrementally — start with vector search, add re-ranking, then add hybrid.
Evaluation
Use RAGAS (RAG Assessment) framework to measure: faithfulness (is the answer grounded in the context?), answer relevance, context precision, and context recall. Target >80% on faithfulness and >70% on answer relevance before shipping.