Turn Your Company Knowledge into an Assistant (Hands-on & Beginner welcome!)
Details
Every company sits on a mountain of knowledge — in files, wikis, and people’s heads.
Accessing it usually means asking someone who “just knows.”
But what if your AI could know too?
Imagine a chatbot that can instantly answer questions about your company and data you have:
- “What’s our refund policy?”
- "How does Process X work?"
- “Where’s the template for that report?”
- “What did we promise in the last proposal?”
- "What did we write about cat food in our last 500 blog posts?"
- "What do our contracts say about Y"?
That’s exactly what a Retrieval-Augmented Generation (RAG) system does: it lets an AI search your own content and answer with real, reliable information — not guesses.
We’ll demystify the basics — embeddings, vector stores, chunking — without overwhelming you. Together, we’ll connect real documents so your AI can give relevant, fact-based answers instead of just inventing things.
#### You’ll learn
- What RAG is (in plain language) — and when it beats a normal chatbot
- How embeddings + a vector store make your content searchable for AI
- How to wire everything up in n8n and test the flow end-to-end
#### What we’ll build (live)
A chatbot that answers common questions from your uploaded PDFs — whether it’s internal guides, SOPs, wikis, or even all your blog posts.
#### Who it’s for
Curious beginners, ops or CS teams, and builders who want AI that’s actually useful at work. No coding required!
***
#### You’ll leave with
A working RAG prototype and a clear understanding of how to connect your company’s knowledge to AI — safely, reliably, and without reinventing the wheel.
***
#### Note
We design our workshops to be fun and insightful for everyone — beginners won’t feel lost, and techies will still learn something new.
That said, this session goes a bit deeper than our usual intros.
👉 If you’ve never used n8n before, we recommend watching some beginner tutorial to get familiar with nodes and triggers before the event.