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

The era of AI simply providing answers is over. Now is the age of LLM Agents that make decisions and take actions on their own. This course is an introductory lecture where you learn the core principles and structure of agents by implementing them yourself in just 90 minutes of hands-on practice. With minimal complex theory and a code-focused practical flow, you can directly experience "how AI makes decisions and uses tools." Beyond prompt engineering, let's take the first step into AI automation together.

(4.6) 5 reviews

63 learners

Level Basic

Course period 5 months

multi-agent
multi-agent
LLM
LLM
LangChain
LangChain
AI Agent
AI Agent
LangGraph
LangGraph
multi-agent
multi-agent
LLM
LLM
LangChain
LangChain
AI Agent
AI Agent
LangGraph
LangGraph
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What you will gain after the course

  • # Understanding the Basic Structure and Operating Principles of LLM Agents

  • Methods for Connecting External Tools and Resources (APIs, Search, etc.) to LLMs

  • Implementation of Agent Decision Logic and Conditional Workflows

  • Memory, Human-in-the-loop, Multi-agent, and various other structure practices

  • Build a Working AI Agent in Under 1 Hour

Learn the principles of AI Agents that think and act in just 90 minutes.🤔


AI is now evolving from 'a tool that answers' to 'an agent that acts'.

This is an introductory course where you'll learn the core principles and structure of LLM Agents through hands-on practice in just 90 minutes.

Without complex formulas or lengthy explanations,

You can directly verify through code how AI makes its own decisions and selects the necessary tools to use.

Without complex theory, just 90 minutes is enough.



The Features of This Course

📌 1-Hour Hands-on Practice Course

I've included only the essential core content. It's structured for learning by following along, without complex theory.

📌 Hands-on Practice with the Latest Models

Google Gemini API and ChatGPT API based latest LLM hands-on practice.

📌 From Basics to Practice, All at Once

Loading LLM Model → Tool Binding → Registering Custom Tools → Graph Design: step-by-step practice.

📌 Experience Various Agent Structures

ReAct, conditional branching, memory, human-in-the-loop, multi-agent collaboration, and other cutting-edge architectures are covered.

📌 Structural Understanding Connected to Real-World Practice

You will learn practical designs that can be immediately applied in real-world scenarios, including agent decision logic, data flow, and state management.


💡What Makes This Course Different


🔸 Intensive Practice Completed in Just 90 Minutes

Short but dense.
In just 90 minutes, you can complete the process of AI making decisions and using tools on its own.
Minimal theory, 'hands-on learning through practice'.


🔸 Build an "AI that uses tools" yourself

ChatGPT only provides answers, but in this course, you'll implement AI that independently decides to directly call search, calculation, and analysis tools. Transform a simple conversational model into an 'AI that takes action'.


🔸 Master both AI automation and practical business sense at once

This is not just a course about running code.
You'll learn agent architecture patterns used in real enterprise environments,
and gain practical thinking skills that can be immediately applied to your projects.

We recommend this for:

AI LLM Beginners

Those who want to understand the principles of LLM and learn about Agents for the first time

Busy practitioners / planners /
Those who want to quickly learn the core structure of AI automation systems

In a short time

Those who want to learn the core concepts of Agents
Those who want to understand the principles of agents and implement them directly through 1 hour of hands-on practice


After taking the course

  • You will understand the logic of how LLMs make decisions and use tools on their own.

  • You can create your own AI agent directly with code.

  • LangChain / LangGraph-based Agent logic core flow and structure design methods are learned.

  • I understand the concepts of AI agent memory, conditional branching, and collaboration systems clearly.

  • You'll gain AI automation ideas that can be applied immediately in real-world work.


You'll learn the following content.

🧠 LLM Agent: The Core Structure of AI That Judges and Acts

How does AI understand questions and autonomously select the necessary tools? By directly implementing a LangChain-based Tool Call mechanism, we'll examine through code the process by which agents "make judgments and take actions on their own."

🧠Tool Binding: Connecting LLMs with External Tools

Connect AI to call external functions like search and APIs, rather than just providing answers. Practice binding real tools like Gemini and Tavily Search to LLMs, and building a structure that automatically determines which questions require tool usage.

⚙️ LangGraph: Visually Designing Agent Flows

What happens when you represent an agent's thoughts and actions as a graph? Using LangGraph, you can visually design and execute complex workflows including conditional branching, parallel processing, and feedback loops.


🧍‍♂️ Human-in-the-loop: AI that makes decisions together with humans

Is it okay for AI to make all decisions? We implement a collaborative decision-making structure where humans intervene at critical moments, and practice a hybrid approach that adds human insight to AI's judgment.



🔄 ReAct Agent: The Brain Structure of AI That Thinks and Acts

Practice the ReAct pattern with its Reason + Action structure. Experience firsthand through code how an agent plans and executes "what to do and how to do it" on its own, and understand AI's decision-making logic.


🤝 Multi-Agent Collaboration: Collaborative AI Systems

If multiple agents collaborate rather than a single AI? Agents separated by role exchange information and,

We implement a structure that solves problems through team-like collaboration. We expand to various scenarios such as actual customer support, knowledge management, and content creation.


The person who created this course

Hello, I'm Jinkyu Lee, CEO of HappyAI, passionate about Generative AI and LLM Agents.

I majored in Natural Language Processing and LLM at an AI graduate school, and have since accumulated practical experience in LLM solution development, Private LLM construction, fine-tuning, and multimodal RAG by conducting over 200 AI·RAG projects with Samsung Electronics, Seoul National University, Korea Electric Power Corporation, and others.

Recently, I have been conducting numerous hands-on lectures on LLM-related topics such as RAG, Agent, and fine-tuning for leading domestic companies and public institutions.

This course is designed ❝ so that even beginners can easily learn and follow along with LLM Agents ❞ based on extensive practical experience, structured to quickly learn the essentials through hands-on practice.


📌 Career Summary

  • 2024~ CEO of HappyAI (Operating a Generative AI & RAG specialized company)

  • Completed Ph.D. coursework in AI Graduate School (Major in LLM & Natural Language Processing)

  • Former Invited Researcher at Software Policy & Research Institute

  • former government-funded research institute researcher

  • Over 200 LLM and RAG projects with hands-on experience


📚 Course and Activity Examples

  • KT – LLM-based Agent LLM Development Course

  • Samsung SDS – LangChain & RAG Hands-on Course

  • Seoul Digital Foundation – LLM Theory and RAG Chatbot Development

In addition, I have conducted LLM big data lectures at numerous companies

Notes Before Enrollment

Practice Environment

  • This course conducts practical exercises on Google Colab.

  • Google Gemini API (Free)

  • ChatGPT API (Paid)

Learning Materials

  • I'll provide you with the code link as an Excel file!

Prerequisites and Important Notes

This course is designed so that even beginners can follow along,
but if you know the following content, your learning speed will be much faster.

  • Basic Python Syntax

  • Basic Concepts of LangChain
    If you have a simple understanding of the Chain, Tool, and Prompt structures,
    the hands-on practice will proceed much more smoothly.

  • Basic Knowledge of LLMs
    If you understand how LLMs process input (prompts) and generate responses (output),
    and their basic operating principles, you'll find it easier to understand Agent architecture.


Recommended for
these people

Who is this course right for?

  • AI LLM Beginners – Those who want to expand their LLM utilization beyond ChatGPT level

  • Beginner Developers / Product Managers – Those who want to implement a working agent in code

  • Prompt Engineer / Practitioner – Those who want to understand agent-based workflows

  • Short-Term Intensive Learner – For those who want to quickly learn only the essentials within 1 hour

Need to know before starting?

  • Python Basic Syntax

  • Having a basic understanding of LLMs (e.g., ChatGPT) will help you grasp this more quickly.

  • It will be easier to understand if you know the basics of Langchain.

Hello
This is HappyAI

4,587

Learners

237

Reviews

51

Answers

4.6

Rating

11

Courses

Lee JinKyu | Lee JinKyu

AI·LLM·Big Data Analysis Expert / CEO of Happy AI

👉You can check the detailed profile at the link below.
https://bit.ly/jinkyu-profile

Hello.
I am Lee JinKyu (Ph.D. in Engineering, Artificial Intelligence), CEO of Happy AI, who has consistently handled AI and big data analysis in R&D, education, and project sites.

I have analyzed various types of unstructured data, such as
surveys, documents, reviews, media, policies, and academic data,
based on Natural Language Processing (NLP) and text mining.
Recently, I have been delivering practical AI application methods tailored to organizations and work environments
using Generative AI and Large Language Models (LLM).

We have collaborated with numerous public institutions, corporations, and educational organizations such as Samsung Electronics, Seoul National University, the Office of Education, Gyeonggi Research Institute, the Korea Forest Service,
the Korea National Park Service, and the Seoul Metropolitan Government,
and have conducted more than 200 research and analysis projects across various domains including healthcare, commerce, ecology, law, economics, and culture.

 


🎒 Inquiries for Lectures and Outsourcing

Kmong Prime Expert (Top 2%)


📘 Bio (Summary)

  • 2024.07 ~ Present
    CEO of Happy AI, a company specializing in Generative AI and Big Data analysis

  • Ph.D. in Engineering (Artificial Intelligence)
    Dongguk University Graduate School of AI

     

    Detailed Major: Large Language Models (LLM)

     

    (2022.03 ~ 2026.02)

     

  • 2023 ~ 2025
    Public News AI Columnist
    (Generative AI Bias, RAG, LLM Application Issues)

  • 2021 ~ 2023
    AI & Big Data specialized company Stellavision Developer

  • 2018 ~ 2021
    Government-funded Research Institute Natural Language Processing & Big Data Analysis Researcher


🔹 Areas of Expertise (Lecture & Project Focused)

  • Generative AI and LLM Utilization

    • Private LLM, RAG, Agent

    • Basics of LoRA and QLoRA Fine-tuning

  • AI-based Big Data Analysis

    • Survey, review, media, policy, and academic data

  • Natural Language Processing (NLP) · Text Mining

    • Topic analysis, sentiment analysis, keyword network

  • Public and Corporate AI Task Automation

    • Document summarization, classification, and analysis

       


🎒 Courses & Activities (Selected)

2025

  • LLM/sLLM Application Development
    (Fine-tuning, RAG, Agent-based) – KT

2024

  • LangChain·RAG-based LLM Programming – Samsung SDS

  • LLM Theory and RAG Chatbot Development Practice – Seoul Digital Foundation

  • Introduction to ChatGPT-based Big Data Analysis – LetUin Edu

  • AI Fundamentals & Prompt Engineering Techniques – Korea Vocational Development Institute

  • LDA & Sentiment Analysis with ChatGPT – Inflearn

  • Python-based Text Analysis – Seoul National University of Science and Technology

  • Building LLM Chatbots Using LangChain – Inflearn

2023

  • Python Basics using ChatGPT – Kyonggi University

  • Big Data Expert Course Special Lecture – Dankook University

  • Fundamentals of Big Data Analysis – LetUin Edu


💻 Projects (Summary)

  • Building a Private LLM-based RAG Chatbot (Korea Electric Power Corporation)

  • LLM-based Forest Restoration Big Data Analysis (National Institute of Forest Science)

  • Internal Network Private LLM Text Mining Solution (Government Agency)

  • LLM Model Development based on Instruction Tuning and RLHF

  • Healthcare, Law, Policy, and Education Data Analysis

  • AI Analysis of Survey, Review, and Media Data

Performed over 200 cases, including public institutions, corporations, and research institutes


📖 Publication (Selected)

  • Improving Commonsense Bias Classification by Mitigating the Influence of Demographic Terms (2024)

  • Improving Generation of Sentiment Commonsense by Bias Mitigation
    – International Conference on Big Data and Smart Computing (2023)

  • Analysis of Perceptions of LLM Technology Based on News Article Big Data (2024)

  • Numerous NLP-based text mining studies
    (Forestry, Environment, Society, and Healthcare sectors)


🔹 Others

  • Python-based data analysis and visualization

  • Data analysis using LLM

  • Improving work productivity using ChatGPT, LangChain, and Agents

More
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4.6

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  • sinkei94564416님의 프로필 이미지
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    GOATGOATGOAT

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        The compact lecture format was good, but it was also disappointing that detailed explanations were inevitably lacking due to this approach.

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