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Chapter 1: The Absolute Basics of Large Language Models

This chapter provides a comprehensive introduction to Large Language Models (LLMs), covering their fundamental concepts, how they work, and the modern ecosystem surrounding them.

Series in This Chapter​

Series 1: What Are LLMs?​

  • Article 1: The "What" and "Why" of LLMs in 2025
  • Article 2: Understanding the Current Model Landscape (GPT-4o, Claude 3.5 Sonnet, Gemini 2.0, Llama 3.1)
  • Article 3: A Brief History of Language Models: From GPT-1 to Multimodal AGI
  • Article 4: Understanding Tokens, Vocabularies, and Context Windows
  • Article 5: The Transformer Architecture: A Deep Dive

Series 2: How LLMs Generate Text​

  • Article 6: How LLMs Generate Text: From Probability to Coherence
  • Article 7: Temperature, Top-p, and Top-k: Controlling Randomness
  • Article 8: Deterministic vs. Stochastic Outputs in Practice
  • Article 9: The Importance of Context Window Size and Management
  • Article 10: Attention Mechanisms and Their Role in Understanding

Series 3: The Modern LLM Ecosystem​

  • Article 11: Different Types of LLMs: A 2025 Comparison
  • Article 12: The LLM Ecosystem: Models, APIs, and Frameworks
  • Article 13: Multimodal LLMs: Vision, Audio, and Beyond
  • Article 14: The Rise of Small Language Models (SLMs) and Edge Computing
  • Article 15: Setting Up Your Environment for Modern Prompt Engineering

Learning Objectives​

By the end of this chapter, you will:

  • Understand what Large Language Models are and why they matter
  • Know the current landscape of available models and their capabilities
  • Grasp the fundamental concepts of how LLMs generate text
  • Be familiar with the modern LLM ecosystem and tools
  • Have a solid foundation for advanced prompt engineering techniques