The AI 104: Introduction to Large Language Models course is your first deep dive into the core technology that powers modern artificial intelligence — systems like ChatGPT, Claude, Gemini, and Copilot. Building on what you learned in AI 101 through AI 103, this course bridges theory and practice, helping you understand what LLMs are, how they work, and why they’ve become the foundation for today’s intelligent applications.

You’ll start by demystifying how LLMs actually function, learning how these models process language, recognize patterns, and generate text that feels remarkably human. From there, you’ll explore the evolution of LLMs, tracing their path from simple statistical models to advanced transformer-based architectures capable of reasoning, summarization, and creativity. The lessons make these complex systems approachable — no coding, math, or data science background required.

Next, you’ll discover how LLMs are being integrated across industries and daily workflows, from customer support and content generation to research, data analysis, and education. You’ll also learn about local language models — private, offline AI systems you can run securely on your own computer — and how they differ from cloud-based tools. This section introduces real-world use cases and demonstrates how open-source LLMs are empowering small businesses, educators, and cybersecurity professionals to work smarter while protecting sensitive data.

Hands-on sessions show you how to create effective prompts and multi-turn conversations with different models, moving beyond simple Q&A interactions. You’ll learn how to guide AI reasoning, build structured outputs, and connect prompts together to create agent-like behaviors that automate tasks or support team workflows. Each module brings the technology down to earth with practical examples you can immediately apply to your job, projects, or business operations.

Finally, you’ll explore the ethical and operational implications of using LLMs responsibly — understanding bias, security, transparency, and model limitations. By the end of AI 104: Introduction to Large Language Models, you’ll have a solid grasp of what makes these systems work, how to use them effectively, and how to prepare for the next stage in your AI education — mastering automation, workflow design, and agent-based reasoning in Cyber-Recon’s advanced courses.

Through engaging lessons, you’ll learn the difference between artificial intelligence, machine learning, and automation in plain, approachable language. You’ll see how AI powers things like recommendation engines, chatbots, and voice assistants, and you’ll explore its growing role in industries from healthcare to finance to creative design. Each module connects technical ideas to real-world outcomes, helping you build a clear, confident understanding without any math or coding required.

You’ll also gain perspective on the opportunities and challenges that come with AI adoption – how data drives intelligence, what “training” really means, and how ethics, bias, and transparency play into responsible development. By the end of AI 101, you’ll be able to separate hype from reality and talk about AI with clarity and confidence. This course lays the foundation for your Cyber-Recon AI certification journey, preparing you for deeper learning in AI 102: AI Basics and AI 103: Prompt Engineering.