Internal GPT is a helpdesk I built for college environments. Administrators upload institutional documents, and students and staff ask natural-language questions. The answers must come strictly from those documents, with source citations.

This post explains how Retrieval-Augmented Generation (RAG) works in plain English, using that project as the example. If you've heard the acronym and nodded without being sure what's inside, this is for you.

The problem RAG solves

A language model knows a lot about the world, but nothing about your college's attendance rules, fee deadlines or hostel policy. If you ask it anyway, it may produce a confident, plausible, completely invented answer. In an institution, an invented policy is worse than no answer.

RAG fixes this by separating knowledge from language :

The documents hold the facts.

The model turns those facts into a readable answer.

The model never has to remember anything.…