Inside an LLM: Parameters & Context Windows Explained | Day 10

You hear the buzzwords every day: "Trillions of parameters" and "Massive context windows." But what do they actually mean? Welcome to Day 10 of Artificial University. Today, we are doing a Part 2 deep-dive into Large Language Models (LLMs) to look at the exact architecture that determines how "smart" an AI actually is.

In Day 7, we learned that LLMs predict the next token. But how does a model remember what you said five pages ago? And what is the physical difference between a small, open-source model and a massive behemoth like GPT-4? In this lecture, we visualize the internal gears of an LLM. We explain how "Parameters" act like the digital synapses of a brain holding vast amounts of knowledge, and how the "Context Window" acts as the model's short-term memory during a conversation.

What you will learn in this lecture:

What is an LLM Parameter, and why does "bigger" usually mean "smarter"?

The visual difference between 7 Billion and 1 Trillion parameters.

What a Context Window is, and how AI remembers your chat history.

The trade-offs: Why running massive LLMs requires so much computing power.

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Next Video (Day 11): What is Data? (The Digital Universe)
Previous Video (Day 9): How AI Understands Your Emotions: NLP Explained

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