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 worksThe difference between RAG and AI fine-tuningHow AI retrieves relevant context from business documents and databasesChunking, embeddings, vector databases, semantic search and citations — explained without the technical overloadWhy current, well-structured documentation matters for reliable AI answersCommon RAG problems, including outdated information, duplicate documents and missing contextHow to start with a smaller RAG project before building a more sophisticated AI knowledge base or internal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 Week03:29 What Is RAG (Retrieval Augmented Generation)07:23 Why General Chatbots Get Your Business Wrong10:03 How RAG Works Under the Hood11:49 Chunking: How RAG Breaks Your Documents Into Pieces12:59 Embeddings and the Vector Database, Explained15:23 Citations: How RAG Shows Its Sources16:13 RAG vs Fine-Tuning: What's the Difference18:25 Real-World RAG: Onboarding and Shared Team Knowledge23:06 Real-World RAG: Making a News Archive Searchable30:18 Optimizing Onboarding Processes with AI30:54 Why RAG Is Really a Documentation Problem31:30 The Importance of Documentation in RAG Systems33:22 Creating a Single Source of Truth34:51 Ensuring Consistency in Data36:23 Understanding AI Limitations and Hallucinations: RAG Failure Modes40:34 How RAG Handles Conflicting Documents and Version Control43:30 Abstention: Why RAG Should Admit It Doesn't Know44:59 How to Build a Simple RAG With Claude, ChatGPT or NotebookLM48:24 Scaling RAG Systems for Larger Teams49:25 Takeaways51:03 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-86c0febba474https://arxiv.org/html/2403.01432v3https://www.ibm.com/think/topics/multimodal-raghttps://memgraph.com/blog/what-is-rag Get in Touchhello@earlyadoptr.aiTikTok: @early_adoptrInstagram: @early_adoptrYouTube: @early_adoptrLinkedIn: https://www.linkedin.com/company/early-adoptr/www.earlyadoptr.ai Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptrTikTok: https://tiktok.com/@early_adoptrYouTube: https://www.youtube.com/@early_adoptrSubstack: https://substack.com/@earlyadoptrpod Hosted on Acast. See acast.com/privacy for more information.