인프런 - 라이프타임 커리어 플랫폼

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!

LLM: A pro quickly delivers core content, from essentials to practical tips!

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.

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.

[Scheduled for completion of the 2nd edition in 2026] Explains why LLM is necessary, the technical background, and basic concepts for LLM planners and LLM application planning managers.

[The 2nd edition is scheduled for completion in 2026] You can develop your programming skills while exploring the fundamental principles of Large Language Models (LLMs) such as ChatGPT and Claude in depth. You can learn a variety of information and development techniques that are often difficult to come by.

"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!

Understand Transformer-based LLM architecture and GPU utilization strategies, and experience direct serving through vLLM. This course covers the entire process of building an AI system pipeline, monitoring, and multi-GPU utilization, allowing you to learn intuitively through illustrations and hands-on practice without complex formulas or coding processes.

What if AI could directly open a browser and write shell scripts to resolve server issues? Through practical automation know-how implemented with OpenClaw across Windows and Linux, we guide you on the shortest path to completing your own powerful remote development environment without complex configurations.

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.

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.

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

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.

[Scheduled for completion of the 2nd edition in 2026] This book provides detailed explanations of various language models developed throughout the process, starting from the beginnings of natural language processing technology to the latest LLM models.

[Scheduled for completion of the 2nd edition in 2026] Explores the limitations of LLMs, the latest technologies to overcome them, and projected future technologies.

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

[2nd Edition scheduled for completion in 2026] We will look at how to collect and analyze user requirements for planning LLM services.

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

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.