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Section 1: Cognitive vs. Computational Processing

Human Thought Versus LLM Processing

How LLMs See the World: Vector Embeddings

The Mechanics of Tokenization

Counting Tokens and Context Windows

🎮 Crossword: Foundations Terminology

Section 2: Architecture and Generation Mechanics

The Transformer Architecture

Auto-Regressive Models Explained

Section 3: Controlling Output and Managing Errors

Probabilities in Language Generation

Temperature and Creativity Settings

Understanding Hallucinations

Mitigating Hallucinations

🎮 Pixel Quest: LLM Mechanics Review

Quiz

Quiz: Human vs. LLM Processing

Quiz: Vector Embeddings

Quiz: Tokenization Basics

Quiz: Context Windows

Quiz: Transformer Basics

Quiz: Attention Mechanisms

Quiz: Auto-Regressive Logic

Quiz: Next Token Prediction

Quiz: Probability Distributions

Quiz: Temperature Settings

Quiz: Low vs. High Temperature

Quiz: Defining Hallucinations

Quiz: Causes of Hallucinations

Quiz: Mitigating Errors

Quiz: LLM Limitations

Survey

Survey: Course Pacing

Survey: Content Clarity

Survey: Topic Relevance

Survey: Technical Depth

Survey: Visual Aids Effectiveness

Survey: Tokenization Module Feedback

Survey: Transformer Module Feedback

Survey: Temperature Module Feedback

Survey: Hallucination Module Feedback

Survey: Quiz Difficulty

Survey: Learning Platform Usability

Survey: Application to Work

Survey: Confidence in Concepts

Survey: Future Topic Requests

Survey: Overall Satisfaction

Decode how modern AI actually works—beyond the hype. Decoding Large Language Models: Architecture, Tokenization and Statistical Processing is a practical, vendor-agnostic course that demystifies how LLMs transform text into numbers, predict the next token, and generate fluent output without true understanding. You’ll learn the foundations that matter: how tokenization shapes costs and capabilities, why embeddings place meaning into high-dimensional space, and how transformers use attention to model long-range dependencies efficiently. We’ll contrast human cognition with computational pattern-matching so you can reason about model limits, error modes, and when to trust (or verify) results. Through clear visuals, interactive mini-games, and quizzes, you’ll practice reading probability distributions, tuning temperature, and estimating token usage to optimize prompts, latency, and spend. By the end, you’ll be able to explain auto-regressive generation in plain English, select parameters for precision or creativity, and build reliable workflows that mitigate hallucinations. Whether you’re a product manager, data scientist, engineer, or curious professional, this course gives you a crisp mental model of how LLMs read, think (statistically), and respond—so you can design better prompts, make smarter integration decisions, and communicate LLM trade-offs with confidence.

2. Decoding Large Language Models: Architecture Tokenization and Statistical Processing

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            <p>The rapid advancement of Large Language Models (LLMs) has fundamentally altered the landscape of artificial intelligence, necessitating a rigorous understanding of their underlying mechanics for those who seek to implement them effectively. This course provides a comprehensive examination of the architectural and statistical frameworks that enable these models to simulate human-like text generation. By contrasting biological cognitive reasoning with the high-dimensional vector processing used by artificial neural networks, participants will gain a clear perspective on why LLMs operate through pattern-matching and statistical retrieval rather than true consciousness or intent. This foundational knowledge is essential for any professional seeking to bridge the gap between linguistic concepts and mathematical execution.</p>

<p>Beyond theoretical distinctions, the curriculum delves into the technical intricacies of the Transformer architecture and the auto-regressive generation process. We will explore the critical role of tokenization—specifically how raw text is converted into numerical data—and the constraints imposed by context windows on model performance and cost-efficiency. Furthermore, the course addresses the practical challenges of managing model outputs, including the manipulation of temperature settings to balance creativity and determinism, and the systematic mitigation of hallucinations. For developers, data scientists, and business analysts, mastering these variables is crucial for optimizing AI-driven workflows and ensuring the reliability of generated content in professional environments.</p>
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