How I Built a Multi-Tenant RAG Knowledge Base with Source-Cited Answers — Pipeline, Multi-Tenancy, and Lessons

Every "build a RAG chatbot" tutorial ends the same way: embed a few paragraphs, call similaritySearch, print the answer. That gets you a demo, not a product. The gap between a RAG demo and a RAG product you'd trust with a company's documents is where all the real engineering lives.

I built KnowBase AI, a multi-tenant SaaS knowledge base where businesses upload documents and an AI assistant answers questions grounded in their own content — with source citations you can click. This post covers the RAG pipeline, how multi-tenancy changes the design, and the decisions I'd repeat.

Live demo: knowbase-ai.netlify.app — no login needed, fully functional (it runs in demo mode with mock responses).

What RAG actually requires

RAG sounds simple: retrieve relevant context, feed it to the LLM, get a grounded answer. In production it means:

How I Built a Multi-Tenant RAG Knowledge Base with Source-Cited Answers — Pipeline, Multi-Tenancy, and Lessons

Related reading

How to Build a RAG System with Your Own Documents in 7 Simple Steps

Building a RAG System From Scratch — Four Components, One Working Pipeline

Build a RAG Pipeline From Scratch (Production Patterns That Actually Matter)

How to Build a RAG Knowledge Base from Any Documentation Site in 5 Minutes

Designing an End-to-End RAG Architecture from Scratch

AWS aims to take the pain out of RAG with Bedrock Managed Knowledge Base