LLM Architecture: Internal Mechanisms, RLHF, and Token Prediction Explained

Have you ever wondered what the actual difference is between a raw completion model and a sophisticated chat model like ChatGPT?

In this video, we break down the internal mechanisms of Large Language Models (LLMs) to show you exactly how they process text. We move beyond high-level theory and look at “Integrated Simulations”—step-by-step visualizations of how models predict tokens, handle memory, and manage safety.

We explore the role of “System,” “User,” and “Assistant” messages, and how Reinforcement Learning from Human Feedback (RLHF) transforms a raw text predictor into a helpful assistant.

In this video, you will learn:

• The fundamental difference between text continuation and conversational response.
• How completion models predict probabilities token-by-token.
• How chat models structure data internally to simulate a conversation.
• The critical role of RLHF in model alignment and safety.
• Why chat models have “memory” and completion models don’t.

If you are a developer, data scientist, or AI enthusiast looking to understand the “black box” of LLMs, this deep dive is for you.