Building Production-Ready Generative AI Applications with LLMs

We will learn step by step from the basic concepts of LLM (Large Language Model) to how to fine-tune the Llama 2 model, a high-performance LLM, on the dataset of your choice.

[Strategic Roadmap for System Control and Prevention of Cognitive Atrophy in the AI Era] 1. Introduction: Technical Initiative and Strategic Command (Strategic Command vs. Passive Dependence) A core insight derived from the past 40 years of automotive R&D and corporate management is that entities that lose technical control are highly likely to devolve from beneficiaries of a system into its dependents. In particular, the proliferation of Artificial Intelligence (AI) technology—comparable to high-performance engines—presents a crossroads: it will either leave humans as "passive passengers" of technology or allow them to leap forward as "strategic commanders" who master the system. The indiscriminate dependence on AI observed today is accelerating "Cognitive Offloading," a phenomenon where humans entirely delegate their inherent thinking and analytical mechanisms to machines. This induces the deactivation of the brain's Executive Control Network (ECN) and carries the risk of a structural crisis known as "Cognitive Atrophy," leading to the functional decline of the frontal lobe in the long term. This course aims to present a strategic methodology to strengthen human cognitive capabilities and safeguard intellectual sovereignty in response to this intellectual crisis. 2. Five Core Methodologies for Safeguarding Cognitive Sovereignty ① Maintaining Cognitive Plasticity and Designing Intentional Cognitive Load (Cognitive Gym) The convenience of AI, which provides immediate and seamless answers, can lead to a disconnection in thinking and the omission of critical review processes. To prevent this, it is necessary to design intentional "Cognitive Friction" within work processes. By counter-utilizing AI's automation functions to forcibly delay and deepen the human thinking process, advanced training must be conducted to stimulate neuroplasticity and raise the threshold of thought. ② Establishing an Adversarial Verification System Based on Multi-Agent Systems (MAS) Human cognitive systems are susceptible to "Automation Bias," the tendency to uncritically accept AI outputs. To offset this bias, it is effective to operate a "Critique Agent" or a virtual "Red Team" that analyzes and attacks the vulnerabilities of the logic, separate from the primary model that follows the user's instructions. This forces the process of building defensive logic, thereby activating "System 2 (deliberative thinking)" as defined by Daniel Kahneman. ③ Implementation of Literacy-Based Dual-Track and RQTDW Learning Methods The ability to verify digital tools is directly proportional to one's analog foundational thinking system. While maintaining literacy to deeply grasp the context of text, it is recommended to internalize the 5-step RQTDW roadmap: Read (Deep Reading): Perform a multi-faceted identification of the source of information. Question (Questioning): Pose critical questions regarding logical consistency and the validity of premises. Think (Confronting Contradictions): Analyze and contemplate trade-offs and logical gaps between information. Discuss (In-depth Discussion): Diversify points of contention through virtual or actual discussions. Write (Reconstruction): Refine the results of expanded thinking into human-specific language for systemic internalization. ④ Applying the Sandwich Workflow to Clarify Accountability Delegating the entire work process to AI poses a high risk of cognitive paralysis; therefore, it is essential to establish a rigorous workflow that structurally separates the roles of humans and AI. Context Design Phase (Top Bun): Setting the purpose of the task, imposing constraints, and designing the overall architecture must be performed under human leadership. Data Processing Phase (Meat): Tasks involving repetitive and large-scale resources, such as calculating vast data, sorting, and drafting, are delegated to AI. Final Verification Phase (Bottom Bun): Ethical judgment, precise cross-referencing of facts (Fact-check), and final value attribution are returned to the human domain of responsibility to ensure system stability. ⑤ Hallucination Control and Strengthening Epistemic Boundaries via the SIFT Model AI possesses the attributes of a "stochastic parrot," combining tokens based on probabilistic frequency without a substantive understanding of meaning. Therefore, one must be wary of the "illusion of knowledge" created by AI's fluent output. To this end, a 3-step fact-checking protocol and the SIFT model must be strictly applied in practice. The habit of "Lateral Reading"—tracking original sources and contrasting them with external data—becomes a key mechanism to prevent intellectual free-riding on technical convenience. 3. Conclusion: The Strategic Mission of the Hyper-Intelligent Helmsman While the phenomenon of intelligence is manifested through engineering design, the core agent that controls it in a meaningful direction and creates business value remains rigorous human thinking. This masterclass is designed to ensure that students acquire the capabilities of a "Hyper-Intelligent Helmsman" who controls the powerful power source of AI and designs organizational systems. Strengthen the cognitive muscles of individual members and actively respond to technical challenges. When rigorous engineering control is combined with advanced cognitive abilities, AI will finally function as a strategic asset that drives the sustainable growth of both members and the organization.

Beyond the era of simply using AI, we are now in an era that requires people who can design AI. This course is a master roadmap that systematically organizes the entire design map of AI agents, from "Introduction → Structure → Design → Operation." Based on RAG, Tool Calling, Memory, and Multi-Agent structures, you will learn how to design and scale AI agents from an architect's perspective. If you want to grow into someone who designs AI systems rather than just someone who "uses" AI, this course will provide the direction.

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

"Your first step to creating a customized LLM with LoRA-based lightweight fine-tuning!" This is an introductory hands-on course designed so that even those new to LLMs can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading the model → applying data → training → comparing results. In a short time, you'll directly experience the workflow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, and gain an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

Claude Code is rapidly growing as one of the most powerful AI coding agents available today. However, simply installing Claude Code is not enough to maximize productivity. To truly utilize Claude Code effectively, you must understand advanced features such as context management, memory systems, skills, subagents, and agent teams. In this course, we will learn step-by-step, starting from the installation and initial setup of Claude Code to advanced workflows used in real-world practice. Students will learn how to integrate Claude Code with VS Code and how to select and utilize various models. Furthermore, you will master how to perform complex development tasks efficiently using Context Engineering, Memory management, Skills, Subagents, and Agent Teams. After the course, you will be able to use Claude Code not just as a simple AI chatbot, but as an AI development partner that genuinely boosts development productivity. Based on real-world experience in AI development and software projects, the instructor explains how to apply Claude Code to practical work and helps students solve common issues such as context management problems, token waste, and inefficient prompt writing.

Learn how to utilize various local LLMs (Qwen, Gemma) and explore different techniques to efficiently evaluate and improve the performance of LLM systems.

You use ChatGPT, but haven't you found it difficult to explain why it gives certain answers? "I know terms like RAG, Agents, and Fine-tuning... but it's hard to explain them accurately." "I get tongue-tied whenever I hear LLM-related terminology." "Conceptual explanations are always vague during AI meetings." This course was created specifically for people like you. This is a theoretical course designed to help you understand LLMs not just as a 'tool,' but as a 'structure.' It doesn't teach you how to use ChatGPT or Gemini; instead, it establishes the foundation so you can explain exactly why they work the way they do.

Build a Saju (Four Pillars of Destiny) analysis AI agent that integrates Manseryeok, Astrology, Zi Wei Dou Shu, Onomancy (Name Study), and MBTI using n8n workflows and Python algorithms. We will complete a production-level service system from start to finish where 13 specialized agents collaborate to generate professional-grade Saju reports.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

Is this kind of introduction even important right now? Use the DeepSeek R1 model right away.

This is a course covering the GitHub notebooks and bonus content from <Build a Large Language Model from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Build a Large Language Model from Scratch> is the Korean translation of the bestseller <Build a Large Language Model (from Scratch)> (Manning, 2024) by Sebastian Raschka. This book provides a way to learn and utilize the operating principles of large language models by building a complete model starting from scratch with OpenAI's GPT-2 model.

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

Explore how LLM applications are shifting the development paradigm. Understand why LLMs are becoming a sustainable core technology through comparisons with existing technologies, and learn about the essential skills and mindset shifts required for developers. Gain insights from expert instructors to grasp the essence and future direction of LLM application development.

This is a hands-on tutorial on how to integrate with IntelliJ and develop quickly using the Model Context Protocol (MCP).

This is an introductory course where you can experience the entire project workflow from idea to deployment using OpenAI Codex. Instead of complex development theories, you will follow actual work processes to gain practical experience in how AI agents generate files, build websites, and deploy them. Even non-developers can master a new way of working by completing projects with AI agents.

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

Learn the core techniques of 'Prompt Engineering' to get exactly the answers you want from AI. From a complete beginner's first steps to expert practical applications, maximize your AI utilization skills!

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

Do you still code directly these days? Now is the era where even vibe coding is too much hassle! Become an AI automation expert with n8n without coding. A practical automation workflow design guide you can apply immediately at work Action-oriented automation strategies that maximize work efficiency. Build intuitive workflows with n8n and experience workplace innovation