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:
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
30:18 Optimizing Onboarding Processes with AI
30:54 Why RAG Is Really a Documentation Problem
31:30 The Importance of Documentation in RAG Systems
33:22 Creating a Single Source of Truth
34:51 Ensuring Consistency in Data
36:23 Understanding AI Limitations and Hallucinations: RAG Failure Modes
40:34 How RAG Handles Conflicting Documents and Version Control
43:30 Abstention: Why RAG Should Admit It Doesn't Know
44:59 How to Build a Simple RAG With Claude, ChatGPT or NotebookLM
48:24 Scaling RAG Systems for Larger Teams
49:25 Takeaways
51:03 AI News: Dario Amodei on Slowing the AI Frontier
Resources:
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