What is RAG?
Straight answers to the questions beginners actually ask about Retrieval-Augmented Generation.
Read the blog →You asked a question. Here’s the answer.
If you typed “what is RAG” into a search box, you probably didn’t want a whitepaper. You wanted someone to just tell you: RAG — Retrieval-Augmented Generation — is a way of making an AI chatbot look things up before it answers, instead of relying only on what it memorized during training. That’s it. That’s the idea.
This site exists because most explanations of RAG start three levels too deep. Every page here is built around one specific question a real beginner asks, and the answer comes first — in the opening paragraph, in plain English. The details, caveats, and “okay, but why?” follow for readers who want to keep going.
Questions we answer
- What is RAG in simple terms? The one-paragraph version, then the slightly longer version.
- Does ChatGPT use RAG? What’s actually happening when a chatbot searches the web or reads your files.
- RAG vs. fine-tuning — which do you need? Two very different tools that beginners are constantly told to choose between.
- What does a vector database do in RAG? The piece of jargon that scares people off, demystified.
- When does RAG give wrong answers? Because it does, and knowing when is half the battle.
No prerequisites. No math. No assumption that you’ve ever written a line of code. If a term shows up, it gets defined in the same breath.
Latest posts
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What Is RAG in Simple Terms?
RAG (Retrieval-Augmented Generation) means an AI looks up relevant documents before answering. Here's the simple version, with a plain analogy.
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Does ChatGPT Use RAG?
Sometimes. When ChatGPT searches the web or reads your uploaded files, that's RAG in action; plain answers from memory are not. Here's how to tell.
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RAG vs. Fine-Tuning: Which Do You Need?
Need the AI to know your facts? Use RAG. Need it to change how it behaves? Fine-tune. A plain-English rule of thumb, with the exceptions that matter.
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When Does RAG Give Wrong Answers?
RAG fails when it retrieves the wrong passages, the documents are wrong, or the model ignores what it read. The main failure modes, in plain English.