---
title: "What is RAG (retrieval-augmented generation)? Explained simply"
canonical_url: "https://tryiro.com/blog/what-is-rag"
site: "Iro AI"
site_url: "https://tryiro.com"
app_store: "https://apps.apple.com/app/id6759628066"
language: en-US
keywords: ["what is RAG", "retrieval augmented generation", "RAG explained", "RAG AI meaning", "what is retrieval augmented generation"]
date_published: "2026-06-11"
date_modified: "2026-06-11"
reading_time_minutes: 6
author: "Alex Furukawa"
license: "© 2026 Iro AI"
canonical_llm_reference: "https://tryiro.com/llms-full.txt"
pillar: "ai-fluency"
---

# What is RAG (retrieval-augmented generation)? Explained simply

> RAG, or retrieval-augmented generation, is a technique that lets an AI look up real information before answering — so it's more accurate and can cite sources. Here's how it works and why it matters.

**Canonical:** https://tryiro.com/blog/what-is-rag
**Published:** 2026-06-11
**Reading time:** ~6 min
**Author:** Alex Furukawa — Founder of Iro AI

## Key takeaways

- RAG (retrieval-augmented generation) connects an AI model to a real source of information so it retrieves facts before generating an answer.
- It reduces hallucinations and lets the AI cite sources and use up-to-date or private data the base model never saw.
- Perplexity and many AI search and 'chat with your documents' tools use RAG.
- You don't need to build RAG to benefit — knowing it exists tells you why grounded, cited answers are more trustworthy.

## What is RAG?

**RAG (retrieval-augmented generation) is a technique that lets an AI look up real information from a trusted source before it answers, instead of relying only on what it memorized during training.** The model _retrieves_ relevant facts, then _generates_ an answer using them. The result is more accurate, can cite sources, and can use recent or private information the base [language model](/blog/what-is-an-llm) never saw.

## How does RAG work?

In three steps:

- **Retrieve.** When you ask a question, the system searches a knowledge source — the live web, a company's documents, a database — for the most relevant passages.
- **Augment.** It adds those passages to your prompt as context.
- **Generate.** The model writes an answer grounded in that retrieved context, often with citations.

So instead of answering from memory alone, the AI answers from memory _plus_ fresh, relevant facts.

## Why RAG matters

RAG fixes two big weaknesses of language models:

- **Hallucinations.** Grounding answers in retrieved sources makes the AI far less likely to [make things up](/blog/spot-ai-hallucinations).
- **Stale or missing knowledge.** A base model only knows its training data. RAG lets it use today's news or your private files without retraining.

That's why a cited, grounded answer is generally more trustworthy than an unsourced one.

## Examples of RAG

- **[Perplexity](/learn-perplexity)** and other AI search tools — they retrieve live web results and answer with citations.
- **"Chat with your documents" tools** — upload a PDF or connect a knowledge base and ask questions about it.
- **Company assistants** that answer from internal policies and docs.

Anytime an AI shows you sources or answers about your own files, RAG is usually behind it.

## What RAG means for you

You don't need to build RAG to benefit from it — but knowing it exists makes you a smarter user. When an answer comes with citations, you can check it; when it doesn't, be more skeptical. Preferring grounded, sourced tools for anything factual is part of [AI literacy](/blog/what-is-ai-literacy). Want to sharpen the broader skill of using AI well? Start with the free [AI IQ test](/quiz).

## FAQ

**What is RAG in simple terms?**

RAG (retrieval-augmented generation) is when an AI looks up real information from a trusted source before answering, instead of relying only on memorized training data. It retrieves relevant facts, then generates an answer using them — making the response more accurate and citable.

**Why is RAG used?**

RAG reduces hallucinations by grounding answers in real sources, and it lets an AI use up-to-date or private information the base model never saw — without retraining. That's why RAG answers often come with citations.

**What is an example of RAG?**

Perplexity and other AI search tools use RAG to answer with live web citations. 'Chat with your documents' tools and company assistants that answer from internal files are also RAG.

**What is the difference between RAG and a normal AI model?**

A normal language model answers only from what it learned during training. A RAG system first retrieves relevant, current information and adds it to the prompt, so the answer is grounded in real sources rather than memory alone.

## Read next

- [What is an LLM?](https://tryiro.com/blog/what-is-an-llm)
- [How to spot AI hallucinations](https://tryiro.com/blog/spot-ai-hallucinations)
- [What is agentic AI?](https://tryiro.com/blog/what-is-agentic-ai)
- [Take the free AI IQ test](https://tryiro.com/quiz)

## About the author

Alex Furukawa — Founder of Iro AI. Alex Furukawa is the founder of Iro AI, the gamified app for learning to use AI well. He writes about practical AI fluency — prompting, AI tools, and the daily habits that turn AI from a novelty into real leverage.