How RAG Works: Turn Internal Knowledge Into Better AI Answers

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overload Why current, well-structured documentation matters for reliable AI answers Common RAG problems, including outdated information, duplicate documents and missing context How to start with a smaller RAG project before building a more sophisticated AI knowledge base or internal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 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:

https://towardsdatascience.com/the-ultimate-guide-to-rags-each-component-dissected-3cd51c4c0212/ https://bytevagabond.com/post/how-to-build-enterprise-ai-rag/ https://medium.com/aingineer/a-complete-guide-to-implementing-hybrid-rag-86c0febba474 https://arxiv.org/html/2403.01432v3 https://www.ibm.com/think/topics/multimodal-rag https://memgraph.com/blog/what-is-rag

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