AI Explained·June 2026

How RAG works: making AI answer from your own data

RAG lets an AI answer from your own documents instead of guessing from memory. Here's how it works in plain English — and why it stops those confident wrong answers.

RAG — Retrieval-Augmented Generation — is a method that lets an AI answer using your own documents instead of guessing from memory. Before answering, it searches your files for the most relevant passages, then writes the answer using only what it found. That's what stops the confident, made-up answers you get when an AI relies on memory alone.

The library and the librarian

Think of your documents as a library and RAG as a librarian. Ask a question and the librarian doesn't recite from memory — they walk to the right shelf, pull the relevant pages, and answer from those. The AI is the writer; retrieval is the librarian handing it the right pages first.

How AI finds the right pages

RAG turns each chunk of your text into an embedding — a point on a map of meaning. Passages with similar meaning sit close together. Your question is mapped to the same space, and the nearest passages are pulled in. That's why RAG finds the right answer even when your wording doesn't match the document word-for-word.

The two phases

- **Ingest** — your documents are split into chunks, embedded, and stored in a vector database.

Answer — your question is embedded, the closest chunks are retrieved, and the AI answers using only those chunks.

The one mistake to avoid

If you don't explicitly tell the AI to answer only from the retrieved passages, it quietly falls back on memory and starts guessing again. "Use only the information you found" is what keeps RAG honest.

A local AI practitioner in Greater Vancouver can build a RAG assistant trained on your handbook, menu, or policies — usually as a small, fixed-price project.

Frequently asked questions

What does RAG stand for?

Retrieval-Augmented Generation.

Do I need a vector database for RAG?

For anything beyond a tiny set of documents, yes — a vector database makes meaning-based retrieval fast.

Does RAG stop AI hallucinations?

It greatly reduces them by grounding answers in your real documents, as long as the AI is told to answer only from what was retrieved.

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