Think AI just makes things up? You’re right, it often does. But there’s a fix. It’s called RAG, and it helps AI ground its answers in real, relevant information. In this episode, I’ll explain how it works and why it’s essential if you rely on AI for content or communication.

Hello and welcome back to AI SIX – the podcast where I break down how artificial intelligence is changing the game for creatives, communicators and anyone trying to stay relevant in the age of automation. I’m Joanne Sweeney, and today’s episode is about something you’ve probably used without even knowing it: RAG – Retrieval-Augmented Generation.

What is RAG?

Let’s start with the basics.

Most large language models, like GPT, Claude, Gemini, work by predicting the next word in a sentence. They’ve been trained on huge datasets, everything from books to websites, forums, Wikipedia, you name it. But once that training is done, the model doesn’t learn anything new unless it’s retrained. That’s called being a static model.

Here’s where RAG comes in.

Retrieval-Augmented Generation is a technique that lets these models access external information, like documents, PDFs, databases, or web content, at the time of your query. Instead of relying only on what the model remembers from training, RAG lets it retrieve relevant information first, and then generate a better, more accurate answer.

Think of it like giving your AI a library card. Before answering you, it goes and finds the most relevant book or article, reads the section it needs, and then gives you a tailored answer based on that.

So technically, RAG combines two things:

1. Retrieval – searching a database or document store to find relevant pieces of information.

    

2. Generation – using a language model to turn that information into a natural, human-sounding response.

    

Here’s why it’s important.

Accuracy.
Language models are good at sounding smart, but they often make things up. That’s called hallucination. With RAG, you massively reduce hallucination by grounding the output in actual data.

Freshness.
RAG lets you plug in up-to-date content. Want your model to reference a 2024 report from your organisation? Just upload it. Now the model doesn’t need to wait for a full retraining cycle.

Customisation.
You can point the model at your own documents, making the output highly relevant. Think internal comms briefs, policy docs, marketing plans, product specs, anything that helps the model speak your language.

Transparency.
Because the model tells you where it got its information from, often linking back to the original source, users can fact-check. This builds trust, especially when you’re using AI in customer-facing roles or public sector comms.

Let’s make this practical.

Imagine you work in a government department. You want to build a chatbot that can answer citizen queries about grants. You feed in all your PDF guidelines, funding rules, eligibility criteria. With a RAG-based system, the chatbot doesn’t just guess, it pulls answers directly from those documents, then explains it clearly.

Or say you’re a marketer. You’ve got campaign decks, audience research, messaging frameworks. You want help writing a press release. With RAG, your assistant draws on your actual brand material, not random examples from the internet.

There are even RAG systems used in journalism now, pulling from archives, recent articles, FOI documents, to generate summaries, quotes, and timelines with citations.

Let’s peek under the bonnet for a second.

When you run a RAG query, the system breaks your prompt into what’s called a “semantic search”. It doesn’t just look for keywords, it looks for meaning. Then it scans the source documents you’ve connected, this could be a folder on Google Drive, Notion pages, your CRM, anything indexed, and retrieves the top relevant chunks.

Those chunks are passed into the language model as part of the prompt. That means the AI isn’t just predicting blindly, it’s responding with access to facts.

Most tools do this in milliseconds. And it’s already built into a lot of platforms—ChatGPT’s Custom GPTs, Claude’s memory, Microsoft Copilot’s SharePoint integration. So even if you’ve never heard the term RAG, you’ve probably used it.

It’s not perfect, of course.

If your source data is poor, badly written, outdated, or biased, you’ll get poor results. RAG doesn’t fix the problem of garbage in, garbage out.

There’s also the risk of overtrust. Just because an AI says “According to Document X…” doesn’t mean that document is accurate. This is where human oversight still matters.

And technically, implementing RAG at scale, say across an entire government or enterprise, takes careful data management, indexing, and privacy compliance.

So here’s the bottom line.

If you’re building anything with AI, especially for work, don’t just ask, “What model should I use?” Ask, “What should the model know?”

RAG is how you make AI smarter on your terms. You feed it your facts, your voice, your priorities. And in return, it gives you relevant, accurate, tailored output.

That’s why RAG isn’t just a technical trick, it’s a turning point. It’s the bridge between general-purpose AI and useful, grounded AI.

I’ll leave you with this: the best AI tools of the next 12 months won’t be the ones that write faster. They’ll be the ones that know what matters.

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