RAG From Scratch

What's inside

A raw model only knows what it saw in training — the average of the internet. It has never read your work, your customers, or what's converted for you, so it regresses to the mean. RAG (Retrieval-Augmented Generation) fixes that by fetching the few things that actually matter and handing them to the model before it answers — turning a closed-book exam into an open-book one.

This guide walks the full system end to end, using Hookem's architecture as the case study: the six-stage pipeline (ingest, chunk, embed, store, retrieve, augment), the three decisions that make or break retrieval quality, and why more context is not better context.

You get a concrete metadata schema for tagging and filtering sources, four retrieval-quality techniques in the order they pay off, a prompt skeleton that keeps the model grounded, and a six-point build checklist — enough to build a real RAG system, not just understand one.