Post by @datascibykashi

🤖 HOW IT FEELS TO EXPLAIN RAG TO SOMEONE Me: “RAG is basically Retrieval-Augmented Generation.” Them: “Okay… what does that mean?” Me: “Imagine you have an LLM, but instead of relying only on what it already knows, you give it access to your own knowledge base.” Them: “Okay…” Me: “So first we split the documents into chunks.” Them: “Chunks?” Me: “Then we create embeddings.” Them: “Embeddings?” Me: “Store them in a vector database.” Them: “Vector database?” Me: “Then retrieve the most relevant chunks using similarity search…” Them: “Wait, what is a vector?” 😭 Me: “…” RAG explanation has entered Phase 2: 📚 Documents → ✂️ Chunking → 🧠 Embeddings → 🗄️ Vector DB → 🔎 Retrieval → 📝 Context → 🤖 LLM → 💬 Answer Them: “So… it’s basically ChatGPT?” Me: 💀 I need a whiteboard. #RAG #RetrievalAugmentedGeneration #GenerativeAI #creatorsearchinsights #machinelearningengineer