What is RAG and Why Every AI SaaS Needs It
RAG (Retrieval-Augmented Generation) lets LLMs answer questions using your private data — without fine-tuning. Instead of training a custom model (expensive, slow, often overkill), RAG retrieves relevant documents and passes them to the LLM as context.
Use cases: document Q&A, customer support bots trained on your knowledge base, code search, legal contract analysis, medical record summarization.
The RAG Architecture
A RAG pipeline has three phases:
- Ingestion: Split documents into chunks → embed chunks → store in vector DB
- Retrieval: Embed user query → find similar chunks → rank by relevance
- Generation: Pass retrieved chunks + query to LLM → stream response
Choosing a Vector Database
- Pinecone: Managed, serverless, best for production. Starts free.
- Qdrant: Open-source, self-hostable, excellent performance
- pgvector: Postgres extension — if you already use Postgres, start here
- Weaviate: Best for multi-modal (text + images)
Chunking Strategy
Chunking is the most underrated part of RAG quality. Poor chunking = poor retrieval = hallucinations. Best practices: use 512–1024 token chunks with 10–20% overlap, split on semantic boundaries (paragraphs, sections) not arbitrary character counts, store metadata (source, page, date) with each chunk.
Implementation with LangChain.js
Use LangChain.js for the full RAG pipeline in Next.js. It handles chunking, embedding, vector store operations, and LLM chaining with a consistent API across providers.
RAG Quality Metrics
Measure: retrieval precision (are returned chunks relevant?), answer faithfulness (does the answer match retrieved context?), and answer relevancy (does it answer the question?). Use Ragas or TruLens for automated RAG evaluation.