Cognitive Load Management Techniques to Overcome the Limits of RAG Performance

Name: Cognitive Load Management Techniques to Overcome the Limits of RAG Performance
Price: 990000 KRW
Rating: 5 (1 reviews)

We present ways to improve RAG performance based on Cognitive Load Theory. To overcome the limitations of existing RAG enhancement techniques, we synthesize various methods by applying the conceptual framework of cognitive load.

(5.0) 1 reviews

23 learners

Level Intermediate

Course period Unlimited

arigaram

ChatGPT

LLM

RAG

ChatGPT

LLM

RAG

What you will gain after the course

Strategies for understanding and managing LLM context window and token limits
How to generate high-quality chunks and integrate them into a RAG pipeline

📋 Change History

April 22, 2026
- We are undergoing a full revision to the 2nd edition before completing the 1st edition. Existing 1st edition sections will be deleted once their corresponding 2nd edition sections are completed.
September 4, 2025
- I have uploaded about 2/3 of the integrated summaries for each section. I will upload the remaining integrated summaries one by one shortly.
- Section 3 was previously split into Section 3 and Section 4, but since the section numbers in the lecture list and the study materials did not match, potentially causing confusion, I have moved Section 4 to Section 31 (at the very end).
September 1, 2025
- Section 3 has been split into Section 3 and Section 4. As a result, the section numbers and the lesson material numbers may not match. I will correct the lesson materials, re-record the videos, and repost them. I would appreciate your patience.
- We are reorganizing the table of contents to reduce confusion for students. Accordingly, the lessons that were temporarily set to private on August 22nd have been changed back to public.
August 22, 2025
- The lessons belonging to the [Advanced] course (Sections 11 to 30), which are not yet completed, have been changed to private. We plan to release them by section or by lesson as they are completed in the future. We ask for your understanding as this is a measure to reduce confusion for students.

🔥RAG technology that overcomes the limitations of LLMs, and cognitive load management technology that will overcome the limitations of RAG technology

Building artificial intelligence services based on Large Language Models (LLMs) has become the mainstream approach, but limitations exist due to restricted context window sizes and token counts. In particular, within Retrieval-Augmented Generation (RAG) systems, failing to properly manage documents or chunks (fragments of documents) leads to cognitive load on the LLM side, making it difficult to generate optimal responses.
Cognitive load refers to the degree of difficulty in perceiving information based on the amount and complexity of information that a system (including both the human brain and artificial intelligence) must process. When cognitive load increases in an LLM system, information accumulates excessively, blurring the core message, degrading performance, and potentially failing to produce the expected level of response. Therefore, effective cognitive load management is a key factor that determines the quality and stability of LLM-based systems.

🎯 What you can learn from this course

Strategies for managing cognitive load according to LLM context windows and token limits
Methods for generating high-quality chunks and ways to utilize various chunking techniques
Technology that integrates data preprocessing, retrieval, prompt design, and post-processing to build a RAG system
Real-time chunk size adjustment and summary parameter control through dynamic optimization
Application of performance evaluation metrics and methods for writing result reports

Section 1. Course Introduction and Basic Concepts

In the first section, we clarify the overall overview and goals of this course and cover the basic concepts of LLM's context window and cognitive load management. In particular, we will gain a detailed understanding of what cognitive load is and why it is important in an LLM environment, while learning the basics of RAG. Based on theory, we will point out the core topics to be covered in the lecture and help you set a learning direction. Concepts are explained step-by-step so that even beginners can easily follow along, establishing a solid foundation for a natural transition to advanced topics later on.

Section 2. Context Window and Token Limits

This section provides an in-depth analysis of the LLM's context window and tokenization mechanisms. We will examine in detail what tokens are, how they are split, and how they affect model input, while explaining how context window size limitations impact model performance through various use cases. Additionally, you will learn how to calculate costs based on tokens to develop practical skills for real-world system design. Through this process, you will gain a systematic understanding of tokens and context, allowing you to intuitively grasp specific issues in managing cognitive load.

Section 3. Chunking Strategies: Chunk Size and Structure

Effective chunk design is the core of RAG system quality. This section introduces various chunking strategies ranging from fixed-size chunks to paragraph-based, semantic clustering, and hierarchical structures, while providing an in-depth look at the pros, cons, and use cases of each method. Based on an understanding of how chunk size and structure impact cognitive load and context utilization, you will acquire practical know-how for designing the optimal chunking strategy for any given situation. Finally, through hands-on exercises, you will gain experience applying various chunking methods to organically connect theory and practice.

Section 4. High-Quality Chunking Techniques

This section covers more advanced techniques for generating chunks suitable for reducing cognitive load and improving information quality. You will learn various techniques such as smart summarization, merging original text with summaries, embedding-based clustering, meta-tagging, and reflecting query intent, and practice how to combine each technique to create more efficient chunks. Through this, you will develop the capability to generate high-quality chunks that go beyond simple chunking methods to reflect the meaning of information and the intent of the questioner. This is a core strategy to help the LLM provide optimal answers even when dealing with complex documents.

Sections 5–10. Integration into the RAG Pipeline

This section covers the design and integration of RAG systems in earnest. It systematically addresses the entire RAG pipeline process, from preprocessing, similarity search and filtering, chunk restructuring and prompt design, to answer generation and post-processing, as well as hallucination checks and re-injection strategies. You will learn the know-how to focus on generating accurate answers while minimizing cognitive load at each stage through hands-on practice. It focuses on providing practical techniques and problem-solving methods that can be immediately applied in real-world environments.

Sections 11–15. Dynamic Optimization Techniques

This section covers how to dynamically adjust context load and chunk size according to the situation. It provides an in-depth introduction to automation and optimization strategies for intelligent system operation, ranging from question complexity evaluation, dynamic chunk size adjustment algorithms, and adaptive summary parameter tuning to context accumulation management in multi-turn conversations, and system monitoring and feedback loop design. Through this, you will acquire real-time management capabilities to maximize LLM performance while responding to changing demands and complexity.

Sections 16–20. Performance Evaluation Methods and Performance Evaluation Metrics

This section covers various metrics and evaluation methodologies to objectively assess the effectiveness of RAG systems and chunking strategies. You will learn how to identify areas for improvement through multi-faceted system performance measurements, ranging from recall, precision, response latency, cost analysis, token usage efficiency, and user satisfaction to strategy validation via A/B testing. Based on the evaluation results, it provides insights for continuous performance tuning and enhancement, strengthening your data-driven decision-making capabilities.

Sections 21–24. Remaining Challenges and Future Technology Outlook

We discuss the research challenges and future scalability potential that need to be addressed in the fields of RAG and LLM cognitive load management. It covers scenarios such as fully automated chunk optimization, long-term memory integration issues, building RAG systems based on large-scale multimedia documents, and methods for expanding RAG systems to process multimodal information. Through the latest research trends and practical application cases, it provides a clear understanding of future development directions and challenges.

Sections 25–30. Introduction to Project Implementation Methods

In this section, we explain how to carry out a comprehensive project that integrates the theories and techniques learned so far to design, implement, tune, and evaluate an actual RAG system. You will be able to verify your practical capabilities by proceeding through all stages in order—from project topic selection to data collection and preprocessing, chunking strategy design, RAG system integration, performance evaluation, result report writing, and finally, the final presentation and code review. Based on what you learn here, developers will be able to conduct practical projects either in teams or individually, allowing them to fully internalize the content learned in this course.

🏆Expected Effects

Based on the concept of cognitive load, you will gain a clear understanding of LLM context windows and token limits, and acquire strategies to manage them.
By using various chunking and optimization techniques, you can efficiently split and summarize information to maximize LLM performance.
Through hands-on practice of the entire RAG pipeline process, you will acquire the ability to build and tune actual systems.
Through dynamic optimization and performance evaluation, you will learn how to provide stable and high-performance AI services in real-time production environments.
Through the latest research topics and expansion directions, you will understand the trends in AI system development and strengthen your future readiness.

Recommended for
these people

Who is this course right for?

Developers who directly design or operate LLM and RAG systems
AI engineer interested in optimizing large-scale document processing and multi-turn conversation handling

Need to know before starting?

Understanding the basic concepts of Natural Language Processing (NLP)
Understanding the basic operating principles of Large Language Models (LLMs)
Concepts of Tokenization and Context Windows
Basic programming skills (Python language recommended)
(Optional) Experience using artificial intelligence and machine learning models, or experience conducting related projects.

Hello
This is arigaram

696

Learners

Reviews

Answers

4.6

Rating

Courses

I am someone for whom IT is both a hobby and a profession.

I have a diverse background in writing, translation, consulting, development, and lecturing.

Curriculum

All

477 lectures ∙ (46hr 37min)

Course Materials:

Lecture resources

Section 1. <1st Edition> [Basic 1] Course Introduction and Basic Concepts (To be deleted after completing the 2nd Edition section below)

10 lectures ∙ (6hr 50min)

Section 2. <2nd Edition> Section 1. [Basic 1] Course Introduction and Basic Concepts

11 lectures

11. <2nd Edition> Lesson 1-0. Integrated Summary of Section 1
12. <2nd Edition> Lesson 1-1. Course Overview and Objectives
13. <2판> Lesson 1-2. Understanding LLM Context Windows
14. <2nd Edition> Lesson 1-3. Concept of Cognitive Load Management
15. <2nd Edition> Lesson 1-4. What is RAG (Retrieval-Augmented Generation)
16. <2nd Edition> Lesson 1-5. [Appendix] Context Window Length of Major LLMs
17. <2nd Edition> Lesson 1-6. [Appendix] The Impact of Context Window Limits on RAG Systems
18. <2nd Edition> Lesson 1-7. [Appendix] 13 RAG Architecture Design Patterns to Overcome Context Window Limitations
19. <2nd Edition> Lesson 1-8. [Appendix] Limitations of RAG Architecture Design Patterns and the Necessity of Incorporating Cognitive Load Theory
20. <2nd Edition> Lesson 1-9. [Appendix] LLMs from the Perspective of Cognitive Load Theory
21. <2nd Edition> Lesson 1-10. [Appendix] Causes of RAG Failure from a Cognitive Load Perspective

Section 3. <2nd Edition> Section 2. Code implementation practice for [Basic 1]

2 lectures

Section 4. <1st Edition> [Basic 2] Limits of Context Windows and Tokens (To be deleted after completing the 2nd Edition section below)

6 lectures ∙ (1hr 52min)

Section 5. <2nd Edition> Section 3. [Basic 2] Context Window and Token Limits

6 lectures

Section 6. <2nd Edition> Section 4. Code Implementation Practice for [Basic 2]

2 lectures

Section 7. <1판> [기본 3] 청킹 전략: 청크의 크기와 구조 (아래 2판 섹션 완성 후 삭제 예정)

5 lectures ∙ (1hr 37min)

Section 8. <2nd Edition> Section 5. [Basic 3] Chunking Strategy - Size and Structure of Chunks

6 lectures

Section 9. <2nd Edition> Section 6. Code Implementation Practice for [Basic 3]

2 lectures

Section 10. <1st Edition> [Basic 4] High-Quality Chunking Techniques (To be deleted after completing the 2nd edition section below)

7 lectures ∙ (4hr 16min)

Section 11. <2nd Edition> Section 7. [Basic 4] High-Quality Chunk Generation Techniques

7 lectures

Section 12. <2nd Edition> Section 8. Code implementation practice for [Basic 4]

5 lectures

Section 13. <1st Edition> [Basic 5] RAG Pipeline Integration Step 1: Preprocessing (To be deleted after completing the 2nd Edition section below)

8 lectures ∙ (2hr 8min)

Section 14. <2nd Edition> Section 9. [Basic 5] RAG Pipeline Integration Step 1 - Preprocessing

8 lectures

Section 15. <2nd Edition> Section 10. Code Implementation Practice for [Basic 5]

1 lectures

Section 16. <1판> [Basic 6] RAG Pipeline Integration Step 2: Retrieval (To be deleted after completing the 2nd edition section below)

8 lectures ∙ (2hr 13min)

Section 17. <2nd Edition> Section 11. [Basic 6] RAG Pipeline Integration Step 2 - Retrieval (Similarity Search and Filtering)

8 lectures

Section 18. <2nd Edition> Section 12. Code Implementation Practice for [Basic 6]

3 lectures

Section 19. <1판> [Basic 7] RAG Pipeline Integration Step 3: Chunk Restructuring and Prompt Redesign (To be deleted after completing the 2nd edition section below)

8 lectures ∙ (2hr 21min)

Section 20. <2nd Edition> Section 13. [Basic 7] RAG Pipeline Integration Step 3 - Chunk Restructuring and Prompt Redesign

8 lectures

Section 21. <2nd Edition> Section 14. Code Implementation Practice for [Basic 7]

3 lectures

Section 22. <1st Edition> [Basic 8] RAG Pipeline Integration Step 4: LLM Answer Generation and Post-processing (To be deleted after completing the 2nd edition section below)

8 lectures ∙ (1hr 43min)

Section 23. <2nd Edition> Section 15. [Basic 8] RAG Pipeline Integration Step 4 - LLM Answer Generation and Post-processing

8 lectures

Section 24. <2nd Edition> Section 16. Code Implementation Practice for [Basic 8]

3 lectures

Section 25. <1판> [Basic 9] RAG Pipeline Integration Stage 5: Hallucination Check and Re-injection Strategy (To be deleted after completing the 2nd edition section below)

8 lectures ∙ (2hr 5min)

Section 26. <2nd Edition> Section 17. [Basic 9] RAG Pipeline Integration Step 5 - Hallucination Check and Re-injection Strategy

8 lectures

Section 27. <2nd Edition> Section 18. Code implementation practice for [Basic 9]

2 lectures

Section 28. <1판> [Basic 10] RAG Pipeline Integration Practice: Building a RAG System that Responds to Simple Questions (To be deleted after completing the 2nd edition section below)

8 lectures ∙ (2hr 52min)

Section 29. <2nd Edition> Section 19. [Basic 10] RAG Pipeline Integration Practice

8 lectures

Section 30. <2nd Edition> Section 20. Code implementation practice for [Basic 10]

5 lectures

Section 31. <1판> [심화 1] 동적 최적화: 질문 복잡도 평가 (아래 2판 섹션 완성 후 삭제 예정)

8 lectures ∙ (3hr 24min)

Section 32. <2nd Edition> Section 21. [Advanced 1] Dynamic Optimization - Evaluating Question Complexity

8 lectures

Section 33. <2nd Edition> Section 22. Code Implementation Practice for [Advanced 1]

3 lectures

Section 34. <1판> [심화 2] 동적 최적화: 동적 청크 크기 조절 알고리즘 (아래 2판 섹션 완성 후 삭제 예정)

8 lectures ∙ (5hr 0min)

Section 35. <2nd Edition> Section 23. [Advanced 2] Dynamic Optimization - Dynamic Chunk Sizing Algorithm

8 lectures

Section 36. <2nd Edition> Section 24. [Advanced 2] Code Implementation Practice

3 lectures

Section 37. <1판> [심화 3] 동적 최적화: 적응형 요약 파라미터 조절 (아래 2판 섹션 완성 후 삭제 예정)

8 lectures ∙ (5hr 12min)

Section 38. <2nd Edition> Section 25. [Advanced 3] Dynamic Optimization - Adaptive Summary Parameter Adjustment

8 lectures

Section 39. <2nd Edition> Section 26. Code Implementation Practice for [Advanced 3]

2 lectures

Section 40. <1판> [심화 4] 동적 최적화: 멀티턴 대화(다회성 대화)에서의 컨텍스트 누적 관리 I (아래 2판 섹션 완성 후 삭제 예정)

5 lectures ∙ (3hr 8min)

Section 41. <2nd Edition> Section 27. [Advanced 4] Dynamic Optimization - Context Accumulation Management in Multi-turn Conversations I

5 lectures

Section 42. <1판> [심화 4] 동적 최적화: 멀티턴 대화(다회성 대화)에서의 컨텍스트 누적 관리 II (아래 2판 섹션 완성 후 삭제 예정)

5 lectures ∙ (54min)

Section 43. <2nd Edition> Section 28. [Advanced 4] Dynamic Optimization - Context Accumulation Management in Multi-turn Conversations II

5 lectures

Section 44. <1st Edition> [Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-turn Conversations III (Scheduled for deletion after completing the 2nd Edition section below)

6 lectures ∙ (55min)

Section 45. <2nd Edition> Section 29. [Advanced 4] Dynamic Optimization - Context Accumulation Management in Multi-turn Conversations III

6 lectures

Section 46. <2nd Edition> Section 30. [Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-turn Conversations III

2 lectures

Section 47. <2nd Edition> Section 31. [Advanced 4] Code Implementation Practice

3 lectures

Section 48. <2nd Edition> Section 32. [Advanced 5] Dynamic Optimization: System Monitoring and Feedback Loop Design

8 lectures

Section 49. <2nd Edition> Section 33. [Advanced 5] Code Implementation Practice

3 lectures

Section 50. <2nd Edition> Section 34. [Advanced 6] Performance Evaluation: Measuring Recall and Precision

8 lectures

Section 51. <2nd Edition> Section 35. [Advanced 6] Code Implementation Practice

4 lectures

Section 52. <2nd Edition> Section 36. [Advanced 7] Performance Evaluation: Latency Analysis and Cost Analysis

8 lectures

Section 53. <2nd Edition> Section 37. [Advanced 7] Code Implementation Practice

2 lectures

Section 54. <2nd Edition> Section 38. [Advanced 8] Performance Evaluation: Token Usage Efficiency Metrics

8 lectures

Section 55. <2nd Edition> Section 39. [Advanced 8] Code Implementation Practice

4 lectures

Section 56. <2nd Edition> Section 40. [Advanced 9] Performance Evaluation: User Satisfaction Evaluation Methodology

8 lectures

Section 57. <2nd Edition> Section 41. [Advanced 9] Code Implementation Practice

3 lectures

Section 58. <2nd Edition> Section 42. [Advanced 10] Performance Evaluation: Strategy Verification through A/B Testing

8 lectures

Section 59. <2nd Edition> Section 43. Code Implementation Practice for [Advanced 10]

1 lectures

Section 60. <2nd Edition> Section 44. [Advanced 11] Technology Outlook: Research Challenges for Fully Automated Chunk Optimization

8 lectures

Section 61. <2nd Edition> Section 45. [Advanced 11] Code Implementation Practice

3 lectures

Section 62. <2nd Edition> Section 46. [Advanced 12] Technology Outlook: Long-term Memory Consolidation

8 lectures

Section 63. <2nd Edition> Section 47. [Advanced 12] Code Implementation Practice

1 lectures

Section 64. <2nd Edition> Section 48. [Advanced 13] Technology Outlook: Large-scale Document-based RAG Scenarios

8 lectures

Section 65. <2nd Edition> Section 49. [Advanced 13] Code Implementation Practice

1 lectures

Section 66. <2nd Edition> Section 50. [Advanced 14] Technology Outlook: Multimodal Information Processing

8 lectures

Section 67. <2nd Edition> Section 51. [Advanced 14] Code Implementation Practice

1 lectures

Section 68. <2nd Edition> Section 52. [Project 1] Project Topic Selection and Proposal Writing

8 lectures

Section 69. <2nd Edition> Section 53. Code Implementation Practice for [Project 1]

3 lectures

Section 70. <2nd Edition> Section 54. [Project 2] Building a Pipeline for Data Collection and Preprocessing

8 lectures

Section 71. <2nd Edition> Section 55. Code Implementation Practice for [Project 2]

8 lectures

Section 72. <2nd Edition> Section 56. [Project 3] Designing and Implementing Chunking Strategies

8 lectures

Section 73. <2nd Edition> Section 57. [Project 3] Code Implementation Practice

4 lectures

Section 74. <2nd Edition> Section 58. [Project 4] RAG System Integration and Orchestration

8 lectures

Section 75. <2nd Edition> Section 59. Code Implementation Practice for [Project 4]

5 lectures

Section 76. <2nd Edition> Section 60. [Project 5] Performance Evaluation and Evaluation Report Writing

8 lectures

Section 77. <2nd Edition> Section 61. Code Implementation Practice for [Project 5]

4 lectures

Section 78. <2nd Edition> Section 62. [Project 6] Final Presentation and Code Review

8 lectures

Section 79. <2nd Edition> Section 63. Code Implementation Practice for [Project 6]

4 lectures

Section 80. <2nd Edition> Section 64. Supplement to [Basic 3]

8 lectures

Section 81. <2nd Edition> Section 65. [Advanced] Harness Engineering of RAG Pipelines - Reinterpreting into a Three-Layer Structure

8 lectures

Section 82. <2nd Edition> Section 66. [Hands-on] RAG Harness Implementation - Validation Loop, Adaptive Pipeline, and Tool Integration

7 lectures

Published: 06/19/2025

Last updated: 04/30/2026

Reviews

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1 reviews

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Average Rating 4.9
07/12/2025
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Good lecture, thanks!
- arigaram
  Instructor
  07/12/2025
  I'm glad it was a beneficial lecture.

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Basic

Python, Raspberry Pi, Arduino

[Practical AIoT] Perfect Preparation for Smart Mirror Makerthon: LLM, CV, and Hardware Design

kodekorea

[LLM 101] Llama SFT Tutorial for LLM Beginners (feat. ChatApp Poc)

dreamingbumblebee

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

Basic

NLP, ChatGPT, LLM

[LLM 101] Llama SFT Tutorial for LLM Beginners (feat. ChatApp Poc)

dreamingbumblebee

Building an automated stock analysis system without writing a single line of code feat. Claude CLI

skysungsisi0926

How long are you going to manually look through 2,500 stocks every day? Searching through Naver Finance to find surging stocks after the market closes, Reading news to judge whether it's good or bad news, Checking foreign/institutional supply and demand one by one, Opening charts to analyze patterns... Aren't you repeating this every single day? I used to do that too. I spent 2 to 3 hours after work analyzing stocks, and yet I still missed more stocks than I found. With over 2,500 stocks across KOSPI and KOSDAQ combined, it's impossible for a human to see them all every day. --- So, I built a system. I created a system that automatically analyzes 2,500 stocks every day after the market closes. - Automatically collects market prices, supply/demand, and news - AI (Gemini) reads the news and determines if it's positive or negative - Scores stocks out of 15 points based on 6 factors - Selects only the stocks that pass the criteria and calculates entry, stop-loss, and take-profit prices - Sends notifications via Telegram Now, I just check my phone after work. Additionally, every Saturday, the system analyzes its own performance from the past week and automatically adjusts stop-loss lines, take-profit lines, and holding periods. It is a structure where the system learns and improves on its own. --- But I am not a developer. I didn't write the code for this system myself. I gave all the instructions to the AI verbally. "Filter out stocks that rose more than 5% today with a trading volume exceeding 50 billion won." "Send these 3 news articles to Gemini and have it judge if they are positive." "Create a scheduler so this runs automatically every day at 4 PM." When I speak like this, the AI (Claude) generates the code. This is "Vibe Coding." --- In this course, we will build this exact system. Over 58 lessons, we will build the system I actually use every day from start to finish. Starting from data collection, AI news analysis, scoring engine, signal generation, Flask API server, Next.js web dashboard, Telegram automatic notifications, and even the self-learning system. The final product is not a Jupyter Notebook. The final product is a web dashboard and Telegram notifications that actually run every day. You don't need to know how to code. In every lesson, I will show you how to talk to Claude, and if you follow along, you will get the same results. --- Recommended for: - Office worker investors who lack the time to analyze stocks every day - Those who want an automated system but don't know how to code - Those curious about quant/system trading but don't know where to start - Those curious about how to utilize AI in practice --- ⚠️ This course does not guarantee investment returns. This is a programming course on building your own stock analysis tools. Actual investment decisions are the responsibility of the student.

Basic

Python, Flask, Next.js

Building an automated stock analysis system without writing a single line of code feat. Claude CLI

skysungsisi0926

Creating a Professional Saju (Four Pillars of Destiny) AI Agent - Full-stack n8n+Python

nambaksa

Build a Saju (Four Pillars of Destiny) analysis AI agent that integrates Manseryeok, Astrology, Zi Wei Dou Shu, Onomancy (Name Study), and MBTI using n8n workflows and Python algorithms. We will complete a production-level service system from start to finish where 13 specialized agents collaborate to generate professional-grade Saju reports.

Intermediate

Python, n8n, AI

Creating a Professional Saju (Four Pillars of Destiny) AI Agent - Full-stack n8n+Python

nambaksa

Automating AI-generated YouTube Shorts with one click (with n8n)

nightdaycoding

Automate YouTube Shorts creation with AI!! Learn how to automatically create YouTube Shorts using n8n from scratch. We will personally build a workflow that handles everything at once: Text → Image/Music → Video Creation → Uploading. Practice includes distinguishing between test/production modes and tips for saving costs. By the end of the lecture, you will complete 3 automation templates that can be used immediately. We will learn step-by-step, starting from node placement, so you can follow along even without coding knowledge. Join us in automating AI YouTube Shorts production!

Beginner

Python, youtube-api, n8n

Automating AI-generated YouTube Shorts with one click (with n8n)

nightdaycoding

Hands-on! Building Intermediate AI Agent Services with LangChain and LangGraph: From RAG to Multi-Agents

YoungJea Oh

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

Basic

AI, ChatGPT, prompt engineering

Hands-on! Building Intermediate AI Agent Services with LangChain and LangGraph: From RAG to Multi-Agents

YoungJea Oh

Mastering Local Execution of Gemma 4

joheejin

Learn the entire process of running the latest Gemma 4 model directly on your MacBook without the burden of paid API costs. You will master performance maximization techniques using Apple Silicon's Metal API and optimal parameter settings for different VRAM capacities, while gaining the ability to build production-level local AI infrastructure based on FastAPI.

Intermediate

macOS, quantization, AI

Mastering Local Execution of Gemma 4

joheejin

Learn ChatGPT API in one day

yangdongjun

A course that will teach you how to create a website with ChatGPT and take charge of its distribution! Learn even fine-tuning.

Basic

ChatGPT, React, netlify

Learn ChatGPT API in one day

yangdongjun

Create AI Apps Without Coding: Complete Mastery of Dify No-Code

multicoreit

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.

Beginner

prompt engineering, LLM, RAG

Create AI Apps Without Coding: Complete Mastery of Dify No-Code

multicoreit

Building an AI Judge with CrewAI and LLM

arigaram

Intermediate

AI, LLM, AI Agent

Building an AI Judge with CrewAI and LLM

arigaram

12 Beginner Guides for Non-Majors to Develop Profitable Websites in the AI Era

cobaetoo

A selfish era where only those who know how to properly utilize AI can generate profit. Essential web development concepts for profitability and a realistic guide you must know to 'properly' direct AI coding agents. From the mindset of coding without writing a single line of code to realistic methods for building a product with zero budget, and even practical details on business registration and tax processing. This is an AI-driven profitable web programming guide filled with insights gained from the actual field.

Beginner

Service Planning, Monetization, Business Problem Solving

12 Beginner Guides for Non-Majors to Develop Profitable Websites in the AI Era

cobaetoo

[Inflearn Award Bestseller] How to Become an AI Automation Expert Without Coding, The Complete n8n Guide

nambaksa

Do you still code directly these days? Now is the era where even vibe coding is too much hassle! Become an AI automation expert with n8n without coding. A practical automation workflow design guide you can apply immediately at work Action-oriented automation strategies that maximize work efficiency. Build intuitive workflows with n8n and experience workplace innovation

Beginner

n8n, AI, RAG

[Inflearn Award Bestseller] How to Become an AI Automation Expert Without Coding, The Complete n8n Guide

nambaksa

[n8n Workflow Automation] Even automating just 3 repetitive daily tasks will change everything (Beginner's Guide)

pulsecreative

"Work automation... isn't that something only developers do?" "Even if I take an automation course, won't it be difficult without development knowledge?" No, it's not. Even if you aren't a developer and have zero development knowledge, you can apply work automation to your actual tasks. This course was created by me—someone who started as a complete beginner with no coding knowledge—for other complete beginners. Even if you are currently at a level where you've only tried asking questions to AI, you can fully understand and follow along. We will directly build automation cases actually used in the workplace, such as receiving daily automated summary reports of insightful articles and automatically detecting and announcing company anniversaries. Through this process, you will understand the 'methods of work automation' and gradually level up to build your own automation systems. Complete your first automation that can be applied to your work immediately in this course.

Beginner

n8n, ChatGPT

[n8n Workflow Automation] Even automating just 3 repetitive daily tasks will change everything (Beginner's Guide)

pulsecreative

Cognitive Load Management Techniques to Overcome the Limits of RAG Performance

What you will gain after the course

🧭 Precautions

📋 Change History

🔥RAG technology that overcomes the limitations of LLMs, and cognitive load management technology that will overcome the limitations of RAG technology

🔍Course Introduction

🎯 What you can learn from this course

Key Concepts

📚 Introduction by Section

Section 1. Course Introduction and Basic Concepts

Section 2. Context Window and Token Limits

Section 3. Chunking Strategies: Chunk Size and Structure

Section 4. High-Quality Chunking Techniques

Sections 5–10. Integration into the RAG Pipeline

Sections 11–15. Dynamic Optimization Techniques

Sections 16–20. Performance Evaluation Methods and Performance Evaluation Metrics

Sections 21–24. Remaining Challenges and Future Technology Outlook

Sections 25–30. Introduction to Project Implementation Methods

🏆Expected Effects

Recommended for
these people

Hello
This is arigaram

Curriculum

Reviews

arigaram's other courses

Similar courses

Cognitive Load Management Techniques to Overcome the Limits of RAG Performance

What you will gain after the course

🧭 Precautions

📋 Change History

🔥RAG technology that overcomes the limitations of LLMs, and cognitive load management technology that will overcome the limitations of RAG technology

🔍Course Introduction

🎯 What you can learn from this course

Key Concepts

📚 Introduction by Section

Section 1. Course Introduction and Basic Concepts

Section 2. Context Window and Token Limits

Section 3. Chunking Strategies: Chunk Size and Structure

Section 4. High-Quality Chunking Techniques

Sections 5–10. Integration into the RAG Pipeline

Sections 11–15. Dynamic Optimization Techniques

Sections 16–20. Performance Evaluation Methods and Performance Evaluation Metrics

Sections 21–24. Remaining Challenges and Future Technology Outlook

Sections 25–30. Introduction to Project Implementation Methods

🏆Expected Effects

Recommended for these people

HelloThis is arigaram

Curriculum

Reviews

arigaram's other courses

Similar courses

Recommended for
these people

Hello
This is arigaram