The original title is "Practical RAG, Part 1: The Simplest RAG That Actually Works"

Short summary

RAG (Retrieval-Augmented Generation) grounds AI models' answers in your actual data without fine-tuning. This hands-on tutorial builds the simplest working pipeline in ~40 lines of Python—embedding chunks, retrieving similar text, and generating grounded answers. You'll understand how RAG works, why this naive approach succeeds on clean data, and what breaks when facing real-world corpus diversity and chunking challenges.

• •40-line Python RAG implementation using local embeddings and OpenRouter
• •Walks through load → chunk → embed → retrieve → generate pipeline step-by-step
• •Reveals why naive RAG works on toy data but fails on real corpora—preview of upcoming improvements

Generated with AI, which can make mistakes.