Part 3 of our Buzzword Breakdown Series
When you ask a general-purpose AI model a business question, it’ll dig deep… into everything it’s ever read online.
That’s like searching for a needle in a haystack – hoping the needle happens to be in there, and that it’s your needle, not someone else’s idea of one.
But what if you had a neat little box of specialist needles – your policies, your knowledge base, your CRM, your documentation? And the model actually looked there first?
That’s what RAG—Retrieval-Augmented Generation – is all about.
What is RAG? (Plain English version)
It’s a simple idea: Look up useful info before generating an answer.
Here’s how it works:
1. You ask a question
2. The system searches your internal content
3. It finds the most relevant snippets
4. Then the AI uses those to generate a response
5. And shows what it used
That’s how we get AI that sounds smart and stays grounded.
Why it matters:
RAG turns a generic model into a domain expert—fast.
No retraining. No guesswork. No uploading private data to public tools.
It’s one of the most effective ways to:
✔️ Build AI assistants that actually understand your business
✔️ Deliver consistent answers across teams and customers
✔️ Improve accuracy and reduce hallucinations
✔️ Keep knowledge in-house, securely
✔️ Avoid reinventing the wheel—or rewriting the helpdesk
At Methodix, we build AI systems that know where to look. Because if the answer’s in your documents… you really don’t need to go digging in a haystack.