(For Planners) Limitations and Future Prospects of LLMs

Without a single formula or line of code, this penetrates the essence of basic statistics necessary for AI development and application.

By enabling junior developers to understand the entire development process from planning to code writing, deployment, and verification using artificial intelligence as a development tool, it helps them survive and thrive by acquiring the full-spectrum capabilities needed in the AI era.

This course systematically teaches prompt engineering, a core technology for effectively utilizing Large Language Models (LLMs) and generative artificial intelligence. It comprehensively covers everything from foundational theory to practical techniques, as well as the latest application cases and security/ethical issues, providing practical help to LLM-based service developers, data scientists, and AI planners alike.

Problematization is a term that can also be translated as problem-raising or problem-framing. It can also be translated as problem-setting or problem-definition. It is a concept that encompasses the process of questioning known facts such as requests or common sense from new perspectives, defining problems, and working out ways to solve these problems. While problematization should be the starting point of all development, it remains an insufficiently discussed topic in the development field. Carrying out projects or developing programs is, in fact, about creating plans to solve problems. In other words, it is related to problematization. To solve a problem, the problem must first be clearly defined. However, problems are usually given in the form of vague requests. Therefore, having the ability to transform vague requests into clear problems reduces unnecessary 'development waste,' facilitates smooth collaboration, and enables proper understanding of users' real needs. This course helps train 'problem-constructing thinking' through practical cases and tools.

What should be done when building a generative AI or LLM-based RAG (Retrieval-Augmented Generation) system, but the desired performance isn't achieved and there's no suitable solution? This lecture presents methods to improve RAG performance based on Cognitive Load theory. Through this lecture, you will understand the limitations of LLM context windows and learn how to effectively manage cognitive load in RAG systems. It is a practical-level theoretical lecture covering Chunk size and structure design, high-quality Chunk generation techniques, dynamic optimization, performance evaluation, and practical techniques.

You can learn the concepts and methods of 'service design' needed when planning applications or web services built on LLM, a type of generative artificial intelligence (generative AI).

"One name change, code transformed." "The key to readable code, 'naming for readability,' summarized from principles to examples." "Easy-to-read names begin collaboration."

This lecture is designed to be easily understood even if you have no prior knowledge of Secure Coding. After covering fundamental secure coding concepts, it focuses on web service security. This course was originally conducted as a special lecture hosted by OO University. It provides over 160 source codes with extensive comments, pinpointing core topics and techniques that can be applied immediately in the field.

You can quickly understand C language's basic concepts and grammar.

👨‍🍳 Like cooking code, neatly and deliciously! 『Clean Coding』 is a code cooking class for chef-like developers, where they joyfully learn clean coding through a cooking analogy. 🍽️

We introduce basic prompt patterns for coding and advanced API prompt patterns for leveraging artificial intelligence.

Starting from the origins of natural language processing technology, this provides a detailed explanation of the various language models developed in the journey leading up to the latest LLM models.

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.

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

Let's explore the types of applications or web services that can be built using LLMs.

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

This explains the foundational principles of large language models like ChatGPT with a focus on theory.

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

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

You can learn the concepts and methods of 'service design' needed when planning applications or web services built on LLM, a type of generative artificial intelligence (generative AI).

[PM,PO] Blockchain Service Planning NFT Planning and Production Marketing We will teach you how to create NFT art resources yourself, create 10,000 NFTs through Hashlips Art Engine, and launch/market them.

Let's explore the types of applications or web services that can be built using LLMs.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

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.

This comprehensive course teaches you how to build any full-stack application using AI tools like Cursor AI, Claude Code, v0, ChatGPT, and Replit. Learn coding basics, front-end and back-end development, database integration, debugging, and deployment. Perfect for beginners and anyone looking to leverage AI to build real web and mobile apps quickly and efficiently.

If you're wondering what UX Writing is and why it's important, you'll find out by looking at examples of successful apps.

Based on an accurate understanding of black consumers, appropriate countermeasures and strategies are acquired, enabling effective response to black consumers. Furthermore, it is designed to enhance the professionalism of service staff and improve job satisfaction.

A must-have course for designers, service planners, and PMs who want to do their jobs better! We will teach you what you need to know for each digital product, industry, and user journey based on real-life cases.

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!

Have you ever felt frustrated when you were creating a B2B product and there was no content covering it? I will explain the similarities and differences between B2B and B2C products in an easy-to-understand way based on your actual career.

Now, with the power of AI, anyone can easily plan apps and design UIs! It's okay even if you don't know coding or have no UI experience. AI tools like Claude and ChatGPT will turn your ideas into reality. With just a few questions, you can analyze target customers, design app structures, and even automatically generate UI screens. In this lecture, we will teach you step-by-step from the basics so that even those who are new to Figma can follow along. Even without drawing by hand, you can import existing website UIs at once with the HTML to Design plugin, and with AI tools like Google Stitch or readdy.ai, you can create high-quality screens in a few seconds. This lecture is perfect for anyone dreaming of starting a business, wanting to switch careers to a planner, or wishing to communicate with developers. Instead of complex theories, we will only teach practical know-how that you can immediately use. Now, with just an idea, anyone can plan app services!

This course provides UX principles and practical know-how necessary for planning and designing AI products. We will explore user-value-centered AI utilization methods, UX planning and design methods using AI, core UX principles and theories learned through everyday design cases, and UX research and data analysis methods for providing a better experience.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.

In the age of information overload, 'communicating easily' is now the real competitive advantage over 'knowing a lot.' This course goes beyond simply polishing your writing—it develops your 'communication design ability' to identify the core message in a flood of information and deliver it at the right level for your audience. It consists of a compact curriculum of 7 lessons totaling 56 minutes. Say goodbye to boring lectures filled with theory! Through training centered on real-world examples (Before/After), you'll transform into an 'information delivery expert' who communicates best within your organization.

"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!

Have you ever created an IR Deck only to receive feedback that it "lacks persuasion"? In most cases, the problem lies not in the design, but in the structure of the message. Based on real-world experience in IR Deck design, I have outlined the criteria for designing a narrative flow from an investor's perspective. This class is designed to help you grow into a hands-on designer who can architect structure beyond just visuals.