In this AI Masterclass episode, I break down one of the most important beginner concepts you need in 2026 if you’re building AI apps: RAG vs fine-tuning. I keep it simple—RAG (Retrieval-Augmented Generation) means your system fetches external data before answering. That external data can be your company’s private documents, internal knowledge base, or any custom dataset you store in a vector database like Supabase or ChromaDB. The big win with RAG is: no model retraining. You’re basically giving your LLM real-time, dynamic private knowledge, which is why it’s perfect for “chat with documents” and enterprise private chatbots. Then I explain fine-tuning: this is when you train the model on custom data so the knowledge gets baked into the model itself. It’s expensive, time-consuming, and usually requires a larger labeled dataset—but it can deliver high accuracy for a specific task or domain like healthcare or manufacturing. The key differences are straightforward: RAG is “search + answer” and updates instantly, while fine-tuning is training-based and more static. In most real enterprise builds today, you’ll see RAG used heavily because it’s faster to update and easier to operate for private knowledge.