LLMs Are Classifiers: How Language Models Predict the Next Token

In this video, we break down one of the most important ideas behind large language models: there is no magic behind text generation — it’s a classification problem. You’ll see how LLMs take a prompt as input and assign probabilities to every possible next token in their vocabulary, then choose the most likely one. Using simple, visual examples, we show how changing just one word in a prompt can completely change the model’s probability distribution and final output. You’ll learn how to: * Think about LLM text generation as multi-class classification * Understand what “next-token prediction” really means * Interpret token probabilities and why most tokens have near-zero probability * See how context changes model behavior with simple prompt edits * Explore an interactive tool to inspect token probabilities yourself Timestamps: 0:00 - Why LLM text generation feels like magic 0:18 - LLMs as classification problems 0:40 - Tokens as classes and probability distributions 2:19 - Visualizing high- and low-probability tokens 4:25 - Vocabulary size and number of classes 4:42 - How changing one word changes probabilities 6:04 - Interactive tool for exploring token predictions 6:25 - Real-world examples and experimentation Watch this video if you want a clear mental model of how LLMs actually work under the hood, before diving deeper into training, fine-tuning, or deployment. This video is part of the LLM Engineering and Deployment Certification Program by Ready Tensor. Enroll Now: https://www.readytensor.ai/programs/llm-engg-and-deployment/ About Ready Tensor: Ready Tensor helps AI and ML professionals build, evaluate, and deploy real-world intelligent systems through hands-on certifications, projects, and industry-aligned learning. Like the video? Subscribe and let us know what other LLM fundamentals you want us to break down next.