Most RAG tutorials give you 30 lines that work once on a sample PDF, then leave you alone when your pipeline starts hallucinating on the docs that actually matter.

This is the other tutorial. We're going to build a RAG pipeline in Python that handles the parts tutorials skip: chunking strategy, retrieval quality, evaluation, and the failure modes that show up when your document set grows past 50 files.

Total code: under 200 lines. No managed services required. No paid vector database subscription.

What RAG Is and What It Actually Fixes

RAG — Retrieval-Augmented Generation — is the pattern for giving an LLM access to your documents without putting your documents in the system prompt.

The naive approach is context stuffing:

# Don't do this with open ( " all_docs.txt " ) as f : context = f . read () # 200,000 tokens response = client . messages .…