“What’s RAG, and Why Should You Care?”

A minimally nerdy guide to RAG and embedding models in nemo

Have you ever wondered...

... How does generative AI actually generate answers? Where does it source its information? And why does it sometimes hallucinate?

Has something like this happened to you?

You ask an AI to explain your onboarding process or quote from an internal document, and it gets it very wrong... So you think it's broken. But more likely, it's just not retrieving the right information.

In generative AI, information retrieval is called RAG (short for Retrieval-Augmented Generation). (Don’t worry; I’ll keep the jargon light.)

Let me show you how it works and why it matters.

What is RAG?

Think of RAG as the ability to look something up before answering you. Every LLM does that, with some differences.

How nemo does RAG

I keep my RAG in-house, limiting my search to the sources you’ve given me — like your company handbook, knowledge base, PDFs, and FAQs — and use that information to build my response. In contrast, off-the-shelf solutions typically retrieve from a much larger data set.

It’s like this: Without RAG → “I’ll give you my best guess. ”With RAG → “Let me check the facts first.”

That means better answers, less guesswork, and far fewer “AI hallucinations.”

Why it matters for your team

Let’s talk real-world benefits of in-house data sourcing:

1. Use your own knowledge Instead of relying on whatever public model was trained on, I use your content — the policies, processes, and materials you already trust.

2. Stay accurate No one wants a hallucination-riddled conversation with customers or employees. With RAG, I anchor my answers in real documents you provide.

3. Skip the overload You don’t need to copy/paste 20 paragraphs into a chat. Just connect your source once, and I’ll find what’s relevant when you ask.

4. Secure by design When you use nemo, your documents never leave your environment. That’s not just smart — it protects your proprietary information.

How it actually works

(don’t worry — I’ll do the hard part)

You bring the knowledge. I handle the rest.

Here’s what the process looks like from your side:

1. Connect your content – Upload documents or link to your data sources. (Think: Google Drive, AWS; more options on demand.)

2. Let me organize it – I chunk the content into bite-sized pieces that I can search through later.

3. Ask away – When you or your users ask a question, I retrieve relevant content and combine it with language generation to give an accurate, fluent response.

4. Get citations – I’ll even show where I found the answer.

   
   

RAG in Practice: Embedding Models

Behind the scenes, RAG depends on an embedding model to understand and search your documents effectively. Just like there are different LLM engines to choose from, you also have a choice of embedding models.

Not sure which embedding model is best? Don’t worry — you’re not alone. With different options from Claude, OpenAI, Perplexity, and more, it can feel like standing in a forest of acronyms.

That’s why I’ve got a brilliant helper on board: Aura.

Ask Aura About Everything RAG + Embedding Models

Aura is an AI agent trained exclusively on nemo's capabilities, including the ins and outs of model selection. She’s here to help you:

• Pick the best embedding model for your content

• Compare model strengths across OpenAI, Claude, and others

• Understand cost vs. accuracy tradeoffs — fast

• Make confident, informed choices (even if you’re new to this stuff)

You don’t need to understand how vector math works. Tap the Aura icon and ask, “Which embedding model is right for me?” for actionable answers.

A few things to remember

Here are a few friendly pointers to help you get the most from me:

• Garbage in, garbage out – If your docs are messy, unclear, or outdated, I may struggle to find (and provide) the right info. Good source content = great responses.

• Structured beats scattered – I work better with organized docs than screenshots or PDFs full of random notes.

• RAG isn’t a search engine – I’m not just pulling a quote. I’m synthesizing info from multiple sources to answer your specific question.

• You stay in control – You decide what I can access and what I can’t. Always.

So, who is RAG for?

Honestly? You. If you’re a leader, a professional, or a domain expert tired of repeating yourself or watching others waste time digging for answers, then RAG is your secret weapon.

It helps me help you — and keeps your expertise where it belongs: working for you.

Let’s put this to work

When you build an agent with me, I’ll walk you through the whole process. And if you get stuck, Aura is right there to help choose the right model, no tech background or AI lingo required.

→ Start Building With Me: Your knowledge deserves to work as hard as you do. Let’s build something useful with it. — nemo

This post is the result of a thoughtful human/AI collaboration.