90-Minute Complete Course: From LLM Agent Basics to Practice – Learn AI Agents Through Hands-on Experience

You use ChatGPT, but haven't you found it difficult to explain why you get certain answers? "RAG, agents, fine-tuning... I know the terms but find it hard to explain them precisely" "I'm at a loss for words when I hear LLM-related terminology" "Explaining concepts in AI meetings is always vague" This course was created specifically for people like you. This course is a theoretical lecture designed to understand LLMs as a 'structure' rather than a 'tool'. It's not about how to use ChatGPT or Gemini, but about building a framework that allows you to explain why they work the way they do.

This course teaches Python-based text mining: basic theory and practice. It covers fundamental text mining data analysis for practical or thesis writing.

This is a course to master the fundamental theories of LLM and the core technologies of LangChain and RAG. You can easily learn the latest AI technologies used in practice, starting from LLM basics!

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

Instead of explanations focused on fundamental theories about 텍스톰(TEXTOM), we enable you to develop text mining and big data analysis capabilities through practical hands-on exercises.

This is a lecture on learning text mining analysis techniques, big data analysis techniques, word frequency analysis, word cloud visualization, morphological analysis, and topic modeling analysis techniques utilizing Python and ChatGPT, covering how to utilize text mining data analysis techniques necessary for writing papers and basic application methods for research papers.

"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!

This is a course for beginners in stock data analysis. Get started with stock data analysis using Python!

This course is beneficial for those who wish to learn how to use ChatGPT and acquire basic Python grammar. It's also helpful for individuals looking to learn and consolidate basic Python grammar.

This is a free lecture designed to help those who are new to TEXTOM easily acquire text mining and big data analysis skills through hands-on exercises with examples, rather than focusing on fundamental theoretical explanations.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

In this course, you will learn how to design, build, and evaluate advanced RAG systems using the LangChain framework. You will master LCEL, Runnables, advanced retrieval techniques, chunking strategies, cross-encoder reranking, agent-based RAG, tool calling, SQL integration, and safety techniques using NeMo Guardrails. You will also learn how to trace, debug, and deploy a full-stack AI chatbot with LangFuse, React, FastAPI, and Docker.

"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!

You use ChatGPT, but haven't you found it difficult to explain why you get certain answers? "RAG, agents, fine-tuning... I know the terms but find it hard to explain them precisely" "I'm at a loss for words when I hear LLM-related terminology" "Explaining concepts in AI meetings is always vague" This course was created specifically for people like you. This course is a theoretical lecture designed to understand LLMs as a 'structure' rather than a 'tool'. It's not about how to use ChatGPT or Gemini, but about building a framework that allows you to explain why they work the way they do.

This course covers the entire process of designing and implementing generative AI services centered on LangChain 1.0 and LangGraph through step-by-step hands-on practice. Beyond simple LLM calls, you'll directly implement operational AI system architectures that include agent-based architecture, state management, memory, streaming, middleware, and Human-in-the-Loop. Through hands-on practice with document/PDF/web data-based RAG systems, SQL Agent (Chinook DB), tool-calling-based Agents, Supervisor pattern multi-agents, and state machine-based workflows using LangGraph Graph API, you'll build reusable agent pipelines that can be immediately applied in real-world scenarios. Additionally, through structured output (Pydantic-based), agent middleware (Summarization, HITL, Retry, PII protection), and token/step-by-step streaming, you'll complete generative AI applications with the stability, scalability, and controllability required in actual services. 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph 👉 For those who want to implement RAG·Agent as a real service structure, not just a "demo" 👉 This is the optimal course for those who need a realistic practical roadmap covering state-based agents, SQL·document automation, and multi-agent orchestration.

Explains why LLMs are needed, their technical background, and basic concepts.

Packed with LLM Fine-Tuning Know-how, learned from Silicon Valley LLM Project Practitioners.

We will examine methods for collecting and analyzing user requirements to plan LLM services.

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

We're building an AI-powered video conferencing application with real-time AI agents, meeting summary features, and post-call functionality.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

AI Utilization Master Guide for Java Developers! This course breaks the stereotype that AI is exclusive to Python, and grants your Java apps AI's eyes, ears, voice, and coding abilities. Grow into an AI architect based on OpenAI API. Directly develop various AI specialists such as AI writers, translators, artists, and coding partners, and create a multifunctional AI assistant that integrates core features like text summarization, sentiment analysis, image description, Java code generation, voice/text conversion, and image/subtitle generation. Learn to assign roles to AI through system messages and even content moderation for harmful content - this is a golden opportunity to maximize the competitiveness of your existing Java systems.

AI writes on my Slack and Notion for me? Work smarter with MCP that surpasses the limitations of existing AI!

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.

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.

The Latest Trend in Generative AI, AI Agents! How to Stay Ahead with Cursor, which handles all development tasks, and MCP, which expands functionality!

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

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

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.