The Fundamentals of AI Agents: From LLM Principles to AI Agent Design and Practice

If you’ve tried AI but get different results every time, or you’re not sure whether it’s okay to put company materials into it, this course has the answer. Using a five-step process (Choose · Input · Instruct · Verify · Preserve) refined through AI training for employees at more than 20 large companies, you’ll practice safely delegating repetitive tasks to Claude Code and verifying the results against the originals.

AI literacy refers to the "ability for general users, not just technicians, to effectively utilize AI in their practical work and daily lives," as well as the "competency to critically evaluate AI." This is a practice-oriented literacy course designed for non-experts to understand the core knowledge for "utilizing" AI well—covering everything from prompt engineering to security understanding, expansion, automation, and the structure of language models.

Understand the basic concepts of AI and blockchain and learn how to apply them in real business scenarios. Beyond just theory, you will work on practical projects combining both technologies through a hands-on format to acquire the skills to master next-generation technology.

👉 A starting point for those who want to properly understand AI for the first time. If you understand what AI does but aren’t quite sure why it works the way it does, this is the course to start with. In this course, we’ll explain step by step, from the beginning, how artificial intelligence differs from traditional programs and how learning and decision-making take place. Without complicated equations or coding, the course is designed to help you understand the big picture of how AI works.

You may have experienced Claude Code producing different results for the same request each time, violating the rules specified in its instructions, and being overly lenient in approving its own output. While developing platform servers in Pangyo and spending over $1,000 a month on Claude Code, I addressed these issues by building a harness consisting of instructions, Hooks, permissions, and a Sandbox instead of relying on prompts. In this course, you’ll build that harness from scratch, add Subagents, Agent Teams, and headless execution, and leave with a complete setup that you can copy and use as-is in other projects.

This course is structured to help even learners new to data architecture understand the material, progressing from basic data concepts → data structures and design → data modeling → databases → enterprise data architecture → AI data architecture → data architecture in practice and future strategies. In particular, it uses an online shopping company as a recurring case study to connect the foundational concepts covered in the course to real-world data design and AI applications.

Connect the diagnostic basis from the previous lecture to the organization’s improvement plan. Review Shadow AI’s data flows and control application requirements, and write a control design document and red-team report containing observed evidence, impacts, limitations, action owners, and retesting criteria.

You will learn the meanings of tokens and context, terms you need to know before using AI services, and why LLMs are called “generative” AI. Through a simple hands-on exercise using Claude for Desktop, you will process unstructured text data into structured data and create a PPT and dashboard. You will learn everything from installing Claude Code to its basic usage, building a foundation for starting vibe coding.

It has become easier to write code with AI, but it is still important to understand how the services we build are structured and operate. We need to know the overall flow—how the frontend and server connect, where data is stored, and what to consider as the number of users grows—so we can ask the right questions. I also studied by building up my knowledge one step at a time, starting with the basics. In this course, I first lay out the big picture, then suggest a way to learn by connecting the details to it. “The Age of AI One-Click Coding, a New Direction for Development: The Big Picture of System Design” is the introductory course in the System Design series. Across 74 lessons, we explore a broad range of topics, from the basic structure of services to data, communication, security, deployment, and operations. Familiar examples involving orders and reservations, along with diagrams that build up step by step, help explain why each concept matters. You don’t need to memorize every term or understand how to implement everything from the start. The goal of this course is to help you follow along comfortably, like watching an educational program, and build a big-picture understanding: “So, these are the kinds of problems that come up, and these are some ways to solve them.” Along with the videos, you’ll get an HTML textbook containing detailed explanations, diagrams, and sample code. You can follow the overall flow in the videos and revisit anything you’re curious about in the textbook. Practical implementation and in-depth coverage of specific topics will continue in the follow-up series.

Mastering difficult AI with just one single analogy?! A first step into AI principles, understanding through core concepts rather than just terminology.

There may be people who have never taken my lecture, but no one has taken it only once. After teaching in person for so long, I have finally had the opportunity to create this online course. Since the commercialization of AI, I have produced and researched countless pieces of AI content. Based on that knowledge and experience, I have built a logical philosophy of my own and organized it into concepts that are easy for anyone to understand. For those who have been generating AI images without considering the underlying concepts, I will instill a proper foundation in you. Once you take this course, you will be able to create any AI image in the future. Have you never created an AI image before? That’s perfectly fine. You don't need any prior experience in AI image generation. By simply learning and practicing through this lecture, you can become an expert in AI content creation.

A course comprising 5 modules and 18 lessons that transforms generative AI LLM agents that only drain money through API costs into a structure that controls expenses and actually generates margins through model routing, cascading, caching, and loop guardrails. You’ll build everything hands-on, from analyzing real-world cost explosion cases and simulating profitability to designing the architecture. Recommended for AI developers and makers burdened by API costs, as well as anyone looking to operate or plan agent-based services.

We examine AI security incidents and the role of red teams, then set up an LLM practice environment in our own AWS account. From capturing the initial response to checking usage and cleaning up resources, we record everything in the Mission 0 checklist. The prompt injection diagnosis exercise continues in Part 4.

From tokens and context to RAG, agents, and guardrails, we examine how AI services work from a security perspective. We compare the concepts with the provided backend code and organize the areas to inspect across input, retrieval, and tool calls into a single attack-surface map.

Using AI has become easier, but model cards and papers are still difficult to read, even just a few lines. Although you can look up the meanings of individual words, it’s not clear how those words connect to one another. This course builds up around 160 essential terms that frequently appear in AI documents, in the easiest order to understand them, so that in each lesson you can accurately read a single sentence from an actual document.

Have you introduced AI but still find yourself losing time to repetitive tasks? The problem isn't the AI; it's the 'system.' This course guides you through the process of building a team of six AI assistants—including an executive assistant, content assistant, marketing assistant, CRM assistant, and personal productivity assistant—all using Notion AI. Based on my experience automating hundreds of repetitive tasks in real-world business settings, we will build actual workflows from start to finish, covering everything from organizing notes and automatically generating meeting minutes to blog writing, CRM construction, and automated KakaoTalk notifications. After completing this course, you will have a system where your own AI assistant team works automatically, powered solely by a Notion Business plan.

For office workers unsure what to delegate to AI and how to instruct it, we provide practical guidelines based on experience with 16 scheduled tasks.

Understand Transformer-based LLM architectures and GPU utilization strategies, and get hands-on experience with the actual serving process using vLLM. This course covers the entire practical workflow, from building AI system pipelines to monitoring and utilizing multiple GPUs, and is designed to help you understand everything intuitively through diagrams and hands-on exercises—without complex formulas.

You have likely seen many misleading lectures about monetizing AI music. Yes, AI music monetization is possible. It is simply not difficult enough to justify paying expensive tuition, nor is it a way to make easy money. This is an action-oriented course for those who are interested in AI music monetization but feel lost on where to start. Based on my personal experience of creating AI music, organizing albums, distributing tracks, and utilizing YouTube and TikTok, I have structured the workflow—from music generation and album planning to file preparation, distribution, and monetization setup—so you can follow along from the very beginning. This course does not guarantee profits; rather, it is designed to help you experience the entire process of releasing AI music and preparing for monetization from start to finish.

Stop getting student-record planning. I’ll reveal the entire consulting playbook used by a college admissions consultant with 30 years of experience.