Embeddings

If you’ve been working with modern AI systems — particularly in the realm of Large Language Models (LLMs), image embeddings, or recommendation engines — you’ve probably heard of vector databases. But what are they really? And why is everyone in the ML community suddenly so excited about them? Let me break it down in simple terms, along with how I’ve been exploring them in my own projects. 🔍 The Problem: Why Traditional Databases Fall Short Traditional databases (like PostgreSQL or MongoDB) are great when you’re dealing with exact matches or relational queries: ...

Large Language Models (LLMs) like GPT or LLaMA are great at generating text. But there’s a catch: They only know what they were trained on, and that knowledge is frozen at training time. So what happens when you ask them something from after their training cutoff? Or something super niche, like a policy from your internal HR docs? Enter RAG – Retrieval-Augmented Generation. A technique that combines LLMs with a search engine, enabling them to look up facts on the fly. ...