LLM Fundamentals: Architecture, Training, and Emergent Abilities

Video Description

Geekmonks.com https://geekmonks.com/llm-eng/introduction-to-llms What is an LLM ? A Large Language Model often refered as LLM is an AI model (class of AI Systems) trained to understand, generate, and manipulate human language by predicting the next word in a sentence. They learn from patterns in text and not by manually programming rules. They are built using deep neural networks - specifically Transformer models that learn patterns from massive amounts of text data because they are trained at internet scale. The more data and parameters (weights) they have, the more sophisticated their language understanding becomes. This simple training objective enables surprisingly powerful capabilities, such as: Understanding natural language Generating coherent paragraphs Translating languages Solving problems step-by-step Writing or debugging code Summarizing documents Engaging in multi-turn conversations Why are they called “Large” ? The term Large refers to the immense number of parameters (learned connections/weights) within the neural network’s architecture, often ranging from billions to trillions. These models are called “Large” because of this scale, which allows them to perform complex tasks like natural language understanding and generation with high accuracy. Examples: GPT-2:1.5 billion parameters GPT-3:175 billion parameters GPT-4 / GPT-5: widely believed to be hundreds of billions to trillions (not publicly disclosed) Note: A larger parameter count allows LLMs to capture more complex patterns, knowledge, and reasoning structures. How do LLMs work ? LLMs follow a predictable internal pipeline when processing or generating text. They do not understand language the way humans do — instead, they convert text into numbers, run it through many mathematical layers, and generate the next most probable token.