RAG, Actually: Embeddings and Retrieval | AI Builder EP 17 #Shorts

Description

RAG isn't magic. It's a three-stage pipeline: 1. Documents → chunks → embedded into vectors (numbers that capture meaning) 2. Vectors stored in a vector database 3. Your question gets embedded → finds nearest matches → AI answers from your data Embed, store, retrieve. Knowing the mechanics turns you from a user into a tuner. Tools: CustomGPT and Chatbase for no-code. n8n + Pinecone or Supabase for control. 🎥 Cadmus Lab — building in public with AI: ► Channel: https://www.youtube.com/@CadmusLab ► Everything: https://beacons.ai/cadmuslab #AIBuilder #BuildingInPublic #RAG #Embeddings #VectorDatabase #Pinecone #Shorts