What's Actually Happening Inside a RAG Chatbot? Let's Build One

Ever wondered what's actually happening when a chatbot searches your data before it answers? In this tutorial, we crack open a RAG chatbot and build one step by step — no LangChain black box, just Pinecone and plain Python so you can see exactly how each piece works. We'll walk through the whole pipeline in plain English: creating a Pinecone index, upserting your lookup data as vectors, searching that index for relevant matches, and plugging the results into an LLM prompt to generate grounded answers. Then we'll wrap it all in a simple Gradio app so you have something you can actually click around in — no prior RAG or vector database experience needed. This description contains an affiliate link — I may earn a small commission at no extra cost to you. 🔗 RESOURCES Pinecone Home: (affiliate link): https://try.pinecone.io/xjb2ebz4mapw Videos Mentioned: Pinecone and Vector DB Intro -    • What Is a Vector Database? (Pinecone Tutor...   RAG Concepts -    • RAG Explained in 8 Minutes (Beginner's Guide)   LiteLLM -    • Stop Managing Multiple LLM APIs: The LiteL...   💻 Code (Github) https://github.com/simpledevdevin/you...