Build a RAG Pipeline from Scratch in Python: A Step-by-Step Guide

Build a RAG Pipeline from Scratch in Python: A Step-by-Step Guide Turn any folder of documents into an AI that actually knows what it's talking about — no hallucinations, no expensive services, just Python and your own data. Build a RAG Pipeline from Scratch in Python: A Step-by-Step Guide Your chatbot just told a customer that your company offers a 90-day return policy. You don't. You never have. This is the hallucination problem in action — and it's why businesses are terrified of deploying AI on anything that actually matters. Large language models don't know things; they predict what sounds right based on patterns they've seen. They'll cite fake court cases, invent product features, and reference policies that exist only in the statistical space between their training tokens. Retrieval-Augmented Generation (RAG) fixes this by giving your AI something it desperately needs: a cheat sheet. Instead of guessing, the model retrieves actual documents from your data — your policies, your d