Cognitive Load Management Technology Breaking Through the Limits of RAG Performance
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.
강의소개.상단개요.수강생.short
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Hello.
The era of AI writing code has arrived.
However, the task of verifying the code written by AI and directing the path for improvement remains the responsibility of humans.
Furthermore, it has become necessary to know how to utilize AI more extensively in both vertical and horizontal directions.
The horizontal direction refers to a direction that encompasses everything from planning to deployment, and
The vertical direction refers to a direction that can encompass various languages, various frameworks, and various methodologies.
Therefore, a developer with a broad spectrum (that is, a complete spectrum in both vertical and horizontal directions)
will be needed.
However, the education system has not yet been able to keep up with this.
That is why I have opened the "Structural Code Reading Bootcamp" using Claude Code.
At https://code-reading-bootcamp.vercel.app/, you can develop the ability to read code written by both AI and humans, and
You will be able to develop the ability to guide AI on how to improve code.
It includes gamification elements, so you can have fun while tracking your skill improvements.
It is free to use anytime without the need to log in.
Although there are some shortcomings and parts where the questions have not yet been filled in,
I introduce this with the hope that it will be helpful to you.
I would appreciate it if you could use it lightly.
February 10, 2026, Sincerely, Jinsu Park (Arigaram).
