Cognitive Load Management Technology Breaking Through the Limits of RAG Performance

Name: Cognitive Load Management Technology Breaking Through the Limits of RAG Performance
Price: 330000 KRW
Rating: 5 (1 reviews)

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.

(5.0) 1 reviews

17 learners

arigaram

컨텍스트창관리

chunk전략설계

rag시스템구축

llm성능평가및튜닝

동적최적화

ChatGPT

LLM

RAG

Generative AI

What you will gain after the course

Strategies to understand and manage LLM context window and token limitations
How to create high-quality Chunks and integrate them into a RAG pipeline

📋Change History

September 4, 2025
- I've uploaded about 2/3 of the integrated summaries for each section. I'll upload the remaining integrated summaries one by one soon.
- I separated Section 3 into Section 3 and Section 4, but this caused a mismatch between the section numbers in the course list and the class materials, which could lead to confusion, so I moved Section 4 to Section 31 (at the very end).
September 1, 2025
- We have separated Section 3 into Section 3 and Section 4. As a result, the section numbers and lesson material numbers may not match. We will update the lesson materials and re-record the videos before posting them again. Thank you for your patience.
- I'm reorganizing the table of contents to reduce confusion for students. Accordingly, I have made the classes that were temporarily set to private on August 22nd public again.
August 22, 2025
- I have changed the lessons in the [Advanced] course (Sections 11-30) that are not yet completed to private status. I plan to make them public by section or by lesson as they are completed. This is a measure to reduce confusion for students, and I would appreciate your understanding.

🔥RAG Technology That Overcomes LLM Limitations, Cognitive Load Management Technology That Will Overcome RAG Technology's Limitations

Large Language Model (LLM)-based artificial intelligence services have become mainstream, but they face limitations due to restricted context window sizes and token counts. Particularly in RAG (Retrieval-Augmented Generation) systems, failure to properly manage documents or chunks (fragments of documents) can create 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 the human brain and artificial intelligence) must process. When cognitive load increases in LLM systems, information can accumulate excessively, obscuring the core message, degrading performance, and failing to produce responses at the expected level. Therefore, effective cognitive load management is a key factor that determines the quality and stability of LLM-based systems.

Section 1. Course Introduction and Basic Concepts

The first section clearly establishes the overall outline and objectives of this course, covering the fundamental concepts of LLM context windows and cognitive load management. In particular, you'll gain a detailed understanding of what cognitive load is, why it's important in LLM environments, and learn the basics of RAG. Based on theory, we'll touch on the core topics covered in the course, helping you establish a learning direction. Concepts are explained step by step so that even beginners can easily follow along, laying a solid foundation for naturally progressing to advanced topics later on.

Section 2. Context Window and Token Limits

This section provides an in-depth analysis of LLM context windows and tokenization mechanisms. It examines in detail what tokens are, how they are segmented, and how they affect model input, explaining with various examples how context window size limitations impact model performance. Additionally, you'll learn how to calculate costs based on tokens, developing practical insights that can be applied when designing real-world systems. Through this process, you'll gain a systematic understanding of tokens and context, enabling you to intuitively grasp the specific challenges of cognitive load management.

Section 3. Chunking Strategy: Chunk Size and Structure

Effective chunk design is the key to RAG system quality. This section introduces various chunking strategies ranging from fixed-size chunks to paragraph-based, semantic unit clustering, and hierarchical structures, and deeply covers the advantages, disadvantages, and application cases of each method. Based on an understanding of how chunk size and structure affect cognitive load and context utilization, you can acquire practical know-how for designing optimal chunking strategies suited to different situations. Finally, through hands-on practice, you will gain experience applying various chunking methods, organically connecting theory and practice.

Section 4. High-Quality Chunk Generation Techniques

This section covers more advanced techniques for creating chunks that are well-suited to reducing cognitive load and improving information quality. You'll learn various technologies 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'll develop the capability to generate high-quality chunks that go beyond simple chunking methods by reflecting the meaning of information and even the questioner's intent. This is a core strategy for helping LLMs deliver optimal answers even when dealing with complex documents.

Sections 5-10. Integration into RAG Pipeline

This section comprehensively covers RAG system design and integration. It systematically addresses the entire RAG pipeline process, from preprocessing, similarity search and filtering, chunk reconstruction and prompt design, answer generation and post-processing, to hallucination detection and re-injection strategies. Through hands-on practice, you'll learn techniques for each stage that minimize cognitive load while focusing on accurate answer generation. The section emphasizes practical skills and problem-solving methods that can be immediately applied in real-world environments.

Section 11-15. Dynamic Optimization Techniques

This section covers methods for dynamically adjusting 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 assessment, dynamic chunk size adjustment algorithms, adaptive summarization parameter tuning, context accumulation management in multi-turn conversations, to system monitoring and feedback loop design. Through this, you will acquire real-time management capabilities that can maximize LLM performance while responding to changing requirements and complexity levels.

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

This covers various metrics and evaluation methodologies for objectively assessing the effectiveness of RAG systems and chunking strategies. You'll learn how to derive improvement points through multifaceted measurement of system performance, from recall, accuracy, response latency, cost analysis, token usage efficiency, and user satisfaction to strategy validation through A/B testing. It provides insights for continuous performance tuning and advancement based on evaluation results, strengthening data-driven decision-making capabilities.

Sections 21-24. Remaining Tasks and Future Technology Outlook

This discusses research challenges and future expansion possibilities in the fields of RAG and LLM cognitive load management. It covers fully automated chunk optimization, long-term memory integration issues, scenarios for building large-scale multimedia document-based RAG systems, and methods for extending 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 ahead.

Sections 25-30. Introduction to Project Implementation Methods

This section explains how to conduct a comprehensive project that integrates the theories and techniques learned so far to design, implement, tune, and evaluate an actual RAG system. By proceeding step-by-step through all stages—from selecting a project topic to data collection and preprocessing, designing chunk strategies, integrating the RAG system, evaluating performance and writing result reports, and conducting final presentations and code reviews—you can validate your practical capabilities. Based on what you learn here, developers will be able to form teams or work individually to carry out practical projects, and through this process, they will be able to fully internalize the content learned in this course.

🏆Expected Outcomes

Understanding the concept of cognitive load, you will clearly grasp LLM context windows and token limits, and acquire strategies to manage them.
Various chunking techniques and chunk optimization methods can efficiently divide and summarize information to maximize LLM performance.
You will practice the entire RAG pipeline process and develop 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 operational environments.
Understand the development trends of AI systems and strengthen future readiness through the latest research tasks and expansion directions.

Recommended for
these people

Who is this course right for?

Developer directly designing or operating LLM and RAG systems
AI Engineer optimizing large-volume document and multi-turn dialogue processing.

Need to know before starting?

Understanding Basic Concepts of Natural Language Processing (NLP)
Understanding the Basic Working Principles of Large Language Models (LLMs)
Concepts of Tokenization and Context Window
Basic programming skills (Python language recommended)
(Optional) Experience utilizing AI and machine learning models or conducting related projects

Hello
This is

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4.5

Rating

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IT가 취미이자 직업인 사람입니다.

다양한 저술, 번역, 자문, 개발, 강의 경력이 있습니다.

Curriculum

All

312 lectures ∙ (45hr 46min)

Course Materials:

Lecture resources

Section 1. [Basic 1] Course Introduction and Fundamental Concepts

6 lectures ∙ (2hr 59min)

Section 2. [Basic 1] Code Implementation Practice

1 lectures

7. (Upcoming) Lesson 1-6. Practice Implementing LLM Input Length Control Code

Section 3. [Basic 2] Context Window and Token Limitations

6 lectures ∙ (1hr 52min)

8. Lesson 2-0. Integrated Summary of Section 2 (Audio Quality Improvement Scheduled)
52:00
9. Class 2-1. Tokens and Tokenization Mechanisms (Audio Quality Improvement Scheduled)
22:08
10. Lesson 2-2. How the Context Window Works (Audio Quality Improvement Planned)
19:06
11. Lesson 2-3. The Impact of Context Window Size on Model Performance (Audio Quality Improvement Scheduled)
13:02
12. Class 2-4. Calculating Token Budget (Audio Quality Improvement Planned)
05:53
13. (Scheduled) Lesson 2-5. RAG System Token Budget Optimization Strategies

Section 4. Practice implementing code from [Basic 2]

2 lectures

Section 5. [Basic 3] Chunking Strategy: Chunk Size and Structure

6 lectures ∙ (1hr 37min)

Section 6. Supplement to [Basic 3]

8 lectures

Section 7. Practice Implementing Code for [Basic 3]

1 lectures

Section 8. [Basic 4] High-Quality Chunk Generation Techniques

7 lectures ∙ (4hr 16min)

Section 9. Practice Implementing Code from [Basic 4]

4 lectures

Section 10. [Basic 5] RAG Pipeline Integration Stage 1: Preprocessing

8 lectures ∙ (2hr 8min)

Section 11. Practice Implementing Code from [Basic 5]

1 lectures

Section 12. [Basic 6] RAG Pipeline Integration Stage 2: Retrieval (Similarity Search and Filtering)

8 lectures ∙ (2hr 13min)

Section 13. Practice Implementing Code from [Basic 6]

2 lectures

Section 14. [Basic 7] RAG Pipeline Integration Stage 3: Chunk Reconstruction and Prompt Redesign

8 lectures ∙ (2hr 21min)

Section 15. [Basic 7] Code Implementation Practice

2 lectures

Section 16. [Basic 8] RAG Pipeline Integration Step 4: LLM Answer Generation and Post-processing

8 lectures ∙ (1hr 43min)

Section 17. [Basic 8] Code Implementation Practice

3 lectures

Section 18. [Basic 9] RAG Pipeline Integration Step 5: Hallucination Detection and Re-injection Strategy

8 lectures ∙ (2hr 5min)

Section 19. [Basic 9] Code Implementation Practice

2 lectures

Section 20. [Basic 10] RAG Pipeline Integration Practice: Building a RAG System that Responds to Simple Questions

8 lectures ∙ (2hr 52min)

Section 21. [Advanced 1] Dynamic Optimization: Question Complexity Assessment

8 lectures ∙ (3hr 24min)

Section 22. # [Advanced 1] Code Implementation Practice

3 lectures

Section 23. [Advanced 2] Dynamic Optimization: Dynamic Chunk Size Adjustment Algorithm

8 lectures ∙ (5hr 0min)

Section 24. # [Advanced 2] Code Implementation Practice

3 lectures

Section 25. [Advanced 3] Dynamic Optimization: Adaptive Summary Parameter Adjustment

8 lectures ∙ (5hr 12min)

Section 26. # [Advanced 3] Code Implementation Practice

2 lectures

Section 27. # [Advanced 4] Dynamic Optimization: Managing Context Accumulation in Multi-turn Conversations

8 lectures ∙ (2hr 40min)

Section 28. # [Advanced 4] Code Implementation Practice

3 lectures

Section 29. # [Advanced 5] Dynamic Optimization: System Monitoring and Feedback Loop Design

8 lectures ∙ (11min)

Section 30. # [Advanced 5] Code Implementation Practice

3 lectures

Section 31. [Advanced 6] Performance Evaluation: Measuring Recall and Precision

8 lectures ∙ (25min)

Section 32. # [Advanced 6] Code Implementation Practice

4 lectures

Section 33. [Advanced 7] Performance Evaluation: Latency Analysis and Cost Analysis

8 lectures ∙ (21min)

Section 34. # [Advanced 7] Code Implementation Practice

2 lectures

Section 35. [Advanced 8] Performance Evaluation: Token Usage Efficiency Metrics

8 lectures ∙ (10min)

Section 36. # [Advanced 8] Code Implementation Practice

4 lectures

Section 37. [Advanced 9] Performance Evaluation: User Satisfaction Assessment Methodology

8 lectures ∙ (17min)

Section 38. [Advanced 10] Performance Evaluation: Strategy Verification through A/B Testing

8 lectures ∙ (25min)

Section 39. # [Advanced 10] Code Implementation Practice

1 lectures

Section 40. [Advanced 11] Technology Outlook: Fully Automated Chunk Optimization Research Challenge

8 lectures ∙ (13min)

Section 41. # [Advanced 12] Technical Outlook: Long-term Memory Integration

8 lectures ∙ (28min)

Section 42. # [Advanced 12] Code Implementation Practice

1 lectures

Section 43. [Advanced 13] Technical Outlook: Large-Scale Document-Based RAG Scenarios

8 lectures ∙ (21min)

Section 44. # [Advanced 13] Code Implementation Practice

1 lectures

Section 45. [Advanced 14] Technology Outlook: Multimodal Information Processing

8 lectures ∙ (20min)

Section 46. # [Advanced 14] Code Implementation Practice

1 lectures

Section 47. [Project 1] Project Topic Selection and Proposal Writing

8 lectures ∙ (11min)

Section 48. [Project 2] Building a Pipeline for Data Collection and Preprocessing

8 lectures ∙ (18min)

Section 49. Practice Implementing Code for [Project 2]

8 lectures

Section 50. [Project 3] Chunking Strategy Design and Implementation

8 lectures ∙ (29min)

Section 51. Practice Code Implementation for [Project 3]

4 lectures

Section 52. [Project 4] RAG System Integration and Coordination

8 lectures ∙ (24min)

Section 53. # [Project 4] Code Implementation Practice

5 lectures

Section 54. [Project 5] Performance Evaluation and Evaluation Report Writing

8 lectures ∙ (14min)

Section 55. # [Project 5] Code Implementation Practice

4 lectures

Section 56. [Project 6] Final Presentation and Code Review

8 lectures ∙ (23min)

Section 57. # [Project 6] Code Implementation Practice

4 lectures

Published:

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- arigaram
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  I'm glad it was a beneficial lecture.

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Cognitive Load Management Technology Breaking Through the Limits of RAG Performance

What you will gain after the course

🧭Important Notes

📋Change History

🔥RAG Technology That Overcomes LLM Limitations, Cognitive Load Management Technology That Will Overcome RAG Technology's Limitations

🔍Course Introduction

🎯What You'll Learn from This Course

Important Concepts

📚Section Introduction

Section 1. Course Introduction and Basic Concepts

Section 2. Context Window and Token Limits

Section 3. Chunking Strategy: Chunk Size and Structure

Section 4. High-Quality Chunk Generation Techniques

Sections 5-10. Integration into RAG Pipeline

Section 11-15. Dynamic Optimization Techniques

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

Sections 21-24. Remaining Tasks and Future Technology Outlook

Sections 25-30. Introduction to Project Implementation Methods

🏆Expected Outcomes

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Cognitive Load Management Technology Breaking Through the Limits of RAG Performance

What you will gain after the course

🧭Important Notes

📋Change History

🔥RAG Technology That Overcomes LLM Limitations, Cognitive Load Management Technology That Will Overcome RAG Technology's Limitations

🔍Course Introduction

🎯What You'll Learn from This Course

Important Concepts

📚Section Introduction

Section 1. Course Introduction and Basic Concepts

Section 2. Context Window and Token Limits

Section 3. Chunking Strategy: Chunk Size and Structure

Section 4. High-Quality Chunk Generation Techniques

Sections 5-10. Integration into RAG Pipeline

Section 11-15. Dynamic Optimization Techniques

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

Sections 21-24. Remaining Tasks and Future Technology Outlook

Sections 25-30. Introduction to Project Implementation Methods

🏆Expected Outcomes

Recommended for these people

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Curriculum

Reviews

arigaram's other courses

Similar courses

Recommended for
these people

Hello
This is