AI in Practice, No Fluff — Day 8/10 I built a memory system for one of my AI projects. Every conversation and decision gets saved as a journal entry. After a few weeks, I had hundreds of entries. Finding the right one when I needed it was the problem. Keyword search was useless. If I searched for "authentication," I would miss the entry where I wrote about "login flow" or "user credentials." The words were different. The meaning was the same. I needed something that could match on meaning, not just spelling. That something is embeddings. In the first series , I mentioned embeddings as part of how RAG systems prepare your data for retrieval. Today I want to unpack what embeddings actually are, how they work, and why they matter well beyond RAG. A list of numbers that represents meaning An embedding is a list of numbers. That is it. You send a piece of text to an embedding model, and it returns a list of numbers (called a vector) that represents the meaning of that text. The list is long.…