Season 1 · Episode 71 · September 23, 2026 · 57 min

How RAG Works: Turn Internal Knowledge Into Better AI Answers

Show Notes

Your business already has the answers. The problem is that nobody can find them. But RAG (retrieval-augmented generation), can help with that.

In this week’s episode, Jess and Kyle unpack RAG — retrieval-augmented generation — the method that lets an AI model retrieve relevant information from a defined set of documents, databases or research before it generates an answer.

From product information and policies to research archives, client documentation and company knowledge, RAG can give an AI assistant the context it needs to answer questions using the information you have chosen.

They cover how RAG works, why it differs from fine-tuning, the role of chunking, embeddings, vector databases and citations (but don't be worried by the jargon!), and how to start with a smaller RAG project before building something more sophisticated.

They also discuss the biggest AI news story of the year, and the practical AI governance decisions businesses can make now.

You’ll learn:

What RAG is and how retrieval-augmented generation works
The difference between RAG and AI fine-tuning
How AI retrieves relevant context from business documents and databases
Chunking, embeddings, vector databases, semantic search and citations — explained without the technical overload
Why current, well-structured documentation matters for reliable AI answers
Common RAG problems, including outdated information, duplicate documents and missing context
How to start with a smaller RAG project before building a more sophisticated AI knowledge base or internal AI assistant

Nishma Patel Robb: https://www.linkedin.com/in/nishmapatelrobb/

Timestamps:

00:43 What We've Been Up To This Week

03:29 What Is RAG (Retrieval Augmented Generation)

07:23 Why General Chatbots Get Your Business Wrong

10:03 How RAG Works Under the Hood

11:49 Chunking: How RAG Breaks Your Documents Into Pieces

12:59 Embeddings and the Vector Database, Explained

15:23 Citations: How RAG Shows Its Sources

16:13 RAG vs Fine-Tuning: What's the Difference

18:25 Real-World RAG: Onboarding and Shared Team Knowledge

23:06 Real-World RAG: Making a News Archive Searchable