Chapter 1: Introduction to AI and LLMs

Is this Chapter for You?

One of the core needs for technical professionals is to keep up with emerging technologies that are transforming how we build and deploy software. AI and LLMs represent a fundamental shift in what’s possible with code, but they come with their own concepts, terminology, and best practices – it can be overwhelming to know where to start!

This beginner-friendly chapter is specifically designed for technical professionals who want to understand and work with AI and LLMs effectively.

  • Are you a developer, engineer, or technical professional looking to understand how to integrate AI into your applications?

  • Do you want to grasp the core concepts and terminology behind LLMs like GPT-4o, Claude, DeepSeek, Gemini, and others to make informed implementation decisions?

  • Do you need to understand both the capabilities and limitations of AI and LLMs to design better technical solutions?

If so, you’ve come to the right place!

How is this Chapter Different?

TL;DR

The primary goal of this chapter is to equip you, a technical professional, with practical AI knowledge to effectively implement and work with AI technologies. Going beyond just using tools like ChatGPT, Claude, or Gemini, you’ll understand how these systems work and how to build with them.

While most introductory courses focus on showing basic functionality, we assume you have some exposure to widespread tools like ChatGPT, Claude, Gemini, and others. These tools are built for the general public and abstract away the underlying complexity. To build effectively with AI, you need to understand what’s happening under the hood.

This chapter focuses on the core concepts and architecture of LLMs, with plenty of practical examples and hands-on exercises. We’ll help you build a strong foundation necessary to go beyond simply using these tools, enabling you to build robust AI-powered applications.

We will introduce essential AI and LLM terminology (with a handy reference glossary), explore key architectural principles, and provide hands-on examples of implementing AI in real-world applications.

What You’ll Learn in This Chapter

By the end of this chapter, you will be able to:

  • Define core AI concepts: Clearly understand AI, Machine Learning (ML), Deep Learning, and Generative AI (GenAI)
  • Understand LLM Architecture: Explain how LLMs process and generate text, with practical implementation considerations
  • Identify Major AI Models: Compare different AI models and understand their trade-offs for various use cases
  • Implement AI Solutions: Build applications using AI APIs with different deployment approaches
  • Create Effective Prompts: Apply prompt engineering techniques to optimize model behavior, including reasoning model optimization
  • Follow Best Practices: Implement AI systems following industry best practices for reliability and performance
  • Understand Agentic AI: Work with AI agents as they exist today in production – from developer tools to business automation

Chapter Topics: Your Introduction to AI and LLMs

Here’s what we’ll cover in this chapter:

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  1. Introduction to AI and LLMs – The 2025/2026 AI landscape: from rule-based systems to multimodal models, reasoning engines, and the open-source explosion

  2. Key Players and Models – Current providers and model families: OpenAI, Anthropic, Google, Meta, DeepSeek, Qwen, Mistral, and the reasoning model revolution

  3. Deployment Considerations – Cloud APIs, self-hosted, edge AI, serverless inference, and hybrid strategies – with cost and security trade-offs

  4. Technical Foundations – Tokenization, embeddings, the Transformer architecture, context windows (200K to 2M+), and memory implementations

  1. Prompt Engineering – Hands-on tutorial: zero-shot, few-shot, chain-of-thought, structured output, and reasoning model optimization techniques

  2. Inference Techniques – Hands-on tutorial: API integration, RAG pipelines, embeddings, streaming, and cost optimization strategies

  1. Agentic AI – AI agents as production reality: Claude Code, Cursor, Devin, n8n, and the spectrum from automation to autonomy – with security implications for Chapter 2
Not Just Theory!

While this chapter provides essential theoretical foundations, we believe in learning by doing. Throughout each section, you’ll find:

  • Interactive Quizzes: Test your understanding of key concepts
  • Hands-on Exercises: Apply what you’ve learned with practical examples
  • Real-world Scenarios: See how these concepts translate to actual business solutions

These practical elements will help you build confidence in applying AI concepts in your professional context.

Learning Progression

This chapter is the foundation of the course. Once you’re comfortable with AI concepts and architectures, continue to Chapter 2: Vulnerabilities and Attacks on LLMs to understand the attack surface of everything you learn here.

Ready to build your AI foundation?

Let’s dive in!