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?? Demystifying How LLMs Work! Credits to 3Blue1Brown If you’ve ever wondered how large language models (LLMs) actually process text, this is hands down one of the most insightful visual explanations out there. ?? Breaking It Down: ?? Tokenization & Embeddings Text is split into tokens (smaller chunks). Each token is mapped into a high-dimensional vector space, grouping similar words together. ?? Self-Attention Mechanism (aka the Secret Sauce) The model understands context—so "bank" in "riverbank" isn’t confused with "financial bank." The attention layers determine how words influence each other dynamically. ?? Feed-Forward Layers (Neural Processing) Tokens pass through multiple layers to refine meaning. Each layer learns deeper semantic relationships, improving the model’s accuracy. ?? Deep Learning & Iteration This process runs through dozens or even hundreds of layers—this is why it’s called deep learning! Every iteration enhances token representations through complex matrix operations. ?? Prediction & Sampling The model predicts the next word as a probability distribution. It then samples from this distribution to generate text word by word. ?? Why This Matters: Understanding these core mechanics is essential if you want to build scalable, responsible AI solutions. This is the foundation of ChatGPT and all other LLMs. ??? Watch the full breakdown here: ?? Video Links Credits to 3Blue1Brown This is a must-watch for anyone in AI—bookmark it for future reference!