About What is RAG?

What is RAG? is an independent question-and-answer site about Retrieval-Augmented Generation — the technique of letting an AI language model retrieve relevant documents before it writes an answer.

The site has one editorial rule that shapes everything: each post answers exactly one question, and the answer appears in the first paragraph. Not after a history of artificial intelligence, not after a diagram of transformer architecture — first. If you stop reading after paragraph one, you should still walk away with a correct, usable answer. Everything after that is optional depth.

Who this is for

People who just met the acronym. Maybe a coworker said “we should use RAG for that,” maybe a vendor pitch mentioned it, maybe a chatbot cited its sources and you wondered how. You don’t need a technical background to read anything here. When a term like embedding, vector database, or context window appears, it’s defined right where it appears.

How we write

• Question-first. Post titles are the literal questions people search. If the honest answer is “it depends,” we say what it depends on.
• Plain English. No math notation, no code required, no jargon left undefined.
• Honest about limits. RAG is genuinely useful and genuinely fallible. We write about its failure modes with the same energy as its benefits.
• Careful with specifics. The AI field moves fast. When something is true as of our writing but likely to change — which products use which techniques, model capabilities, feature names — we say so rather than pretending it’s permanent. We don’t invent statistics or cite sources that don’t exist.

What this site is not

It’s not a course, a consultancy, or a product blog. Nothing is sponsored, nothing is affiliate-linked, and we don’t sell a RAG platform. It’s also not a research digest — for cutting-edge papers you’ll want other sources. This is the friendly first stop: the place that answers the question you actually asked, so the deeper material makes sense afterward.