Articles
Feb 18, 2026
How RAG Works: Giving Your AI a Memory
Language models are trained on a snapshot of the world. Once training ends, their knowledge freezes. Ask GPT-4 about something that happened last month, or ask it about a private internal document it's never seen, and it'll either hallucinate or admit it doesn't know. Retrieval-Augmented Generation — RAG — is the most practical solution to this problem. Instead of baking all knowledge into model weights, RAG lets a model look things up before answering. The Core Idea RAG is made up of two s
Feb 5, 2026
Transformer Architecture: A Visual & Interactive Guide
Token Embeddings 0:00 /0:05 1× The Problem Computers don't understand words — they understand numbers. But how do we convert "cat" into something a neural network can process? Naive approach: Assign each word a number (cat=1, dog=2, …) * Problem: This implies "cat" and "dog" are as different as "cat" and "quantum physics" * We lose all semantic meaning Better approach: Represent each word as a vector in high-dimensional space where: * Similar