Human input is messy. People misspell words, use different forms of the same name, leave out accents, mix languages and enter things that are almost — but not quite — what the application expects. It is tempting to solve all of that by sending every input to an LLM.
For most of the time I have been using language models, the interaction has been very simple: I send text, the model sends text back. Even RAG mostly keeps that pattern. The system retrieves some information, puts it into the prompt and asks the model to produce a better answer.
After building a small RAG app, the concept became much easier to explain. RAG stands for retrieval-augmented generation, but the name makes it sound more complicated than it is. Imagine I have a large document and ask a language model one question about it.
I had heard the term RAG often enough by this point, but I wanted to build the smallest version possible so I could understand what was actually happening. The result was a tiny knowledge-base chat. I paste some text into the app.