Chapter 1: Introduction to AI and LLMs

Work in progress

This course is under construction. This information hasn’t been reviewed or edited yet!

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 coming 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, DeepSeek, Mistral, 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 course 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 code snippets in multiple programming languages. 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 for different applications
  • Follow Best Practices: Implement AI systems following industry best practices for reliability and performance
  • Understand Agentic AI: Grasp the concept of AI agents and their potential future applications

Chapter Topics: Your Introduction to AI and LLMs

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

  1. Introduction to AI and LLMs

    • Evolution of AI from rule-based systems to machine learning and deep learning
    • Core concepts in AI, ML, Deep Learning, and Generative AI
    • Understanding model capabilities, limitations, and hallucinations
    • Societal impact and industry transformation through AI applications

  2. Key Players and Models

    • Overview of commercial LLM providers (OpenAI, Anthropic, Google, etc.) and open-source options (Meta’s Llama, Mistral)
    • Comparing foundation models, fine-tuned models, and specialized models
    • Understanding model selection criteria for enterprise applications
    • Analyzing the lifecycle of LLMs from training to inference

  3. Deployment Considerations

    • Different deployment approaches (hosted, cloud-based, on-premises, edge, hybrid)
    • Security, scalability, and compliance implications
    • Model serialization formats and their security implications
    • Understanding safety guardrails including refusal pathways and moderation endpoints

  4. Technical Foundations

    • Understanding tokenization, embeddings, and the Transformer architecture
    • How context windows function and their practical limitations
    • Exploring different types of memory implementations in LLMs
    • Learning processes including weights, biases, and quality training data

  5. Prompt Engineering

    • Components of effective prompts including task instructions and format specifications
    • Essential parameters like temperature and top-P sampling
    • Advanced techniques including chain-of-thought prompting
    • Optimizing prompts for reasoning models vs. traditional LLMs

  6. Inference Techniques

    • API integration approaches (Completion API vs. Chat API)
    • Response handling strategies (synchronous vs. streaming)
    • Knowledge integration methods including Retrieval-Augmented Generation (RAG)
    • Optimizing inference for performance and cost efficiency

  7. Agentic Future

    • Evolution from generative to agentic AI systems
    • Characteristics that define AI agents (goal-setting, decision-making, autonomous action)
    • Real-world applications and business impact across different sectors
    • Challenges including governance, security, ethics, and workforce implications
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 code 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.

Ready to start learning more about what’s all the fuss about AI?

Let’s dive in!