Comment MODEL and I'll send you the complete LLM training pipeline, from raw text to a working chatbot, with code you can run. How does a model learn from billions of words and then answer a question it has never seen before? Ask it why a metal spoon feels colder than a wooden one. That exact sentence was never in training, so there is no stored answer to look up. ⚡ Teams filter huge text collections, remove duplicates, and balance general text, code, and multilingual material. That mix decides which patterns the model practices most often ⚡ A tokenizer turns every document into tokens, and each token maps to a number. The model then predicts the next token, measures how wrong it was, and the optimizer nudges its parameters slightly, batch after batch ⚡ Pretraining builds a continuation engine. Post-training on examples of useful responses and preference data turns it into an assistant that follows instructions ⚡ When you ask the question, the weights stay frozen. The model produces probabilities, a decoding rule picks one token, and the loop repeats. The answer comes from generalization, with no lookup involved A fluent answer can still be confidently wrong, because next-token prediction rewards plausible text, and plausible is not the same as true. Comment MODEL for the pipeline. Which stage did you think was doing the real work before this video? 👇 #llm #machinelearning #artificialintelligence #aiengineering #deeplearning — @entrepreneurship93 TikTok