Cognitive Load Management Techniques to Break Through RAG Performance Limits (Subtitle: Mastering Context Engineering)

Name: Cognitive Load Management Techniques to Break Through RAG Performance Limits (Subtitle: Mastering Context Engineering)
Price: 990000 KRW
Rating: 5 (2 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) 2 reviews

45 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 proceeding with 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 course materials did not match and could cause 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 revise the lesson materials, re-record the videos, and repost them. I would appreciate your patience.
- We are restructuring the table of contents to minimize confusion for students. Accordingly, the lessons that were temporarily set to private on August 22nd have been changed back to public.
August 22, 2025
- We have changed the status of the unfinished [Advanced] course lessons (Sections 11 to 30) to private. We plan to release them by section or by lesson as they are completed in the future. We appreciate your understanding, as this measure was taken 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

While building AI services based on Large Language Models (LLMs) has become the mainstream approach, there are limitations due to restricted context window sizes and token counts. In particular, within RAG (Retrieval-Augmented Generation) systems, failing to properly manage documents or chunks can lead 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 answers at the expected level. 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

Cognitive load management strategies based on 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 for building RAG systems
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 objectives of this course and cover the basic concepts of LLM context windows 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 review the core topics to be covered in the lecture, helping you set your learning direction. By explaining concepts step-by-step so that even beginners can easily follow along, we provide a solid foundation for a natural transition to more 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. Advanced Techniques for High-Quality Chunking

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 questioner's intent. 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 RAG system design and integration 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 accurate answer generation while minimizing cognitive load at each stage through hands-on practice. It focuses on providing practical techniques and problem-solving methods that can be applied immediately 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, as well as 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 for objectively assessing the effectiveness of RAG systems and chunking strategies. You will learn how to derive improvements through multi-faceted 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 advancement, while strengthening data-driven decision-making capabilities.

Sections 21–24. Remaining Challenges and Future Technology Outlook

We discuss research challenges and future scalability potential 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 skills by proceeding through all stages in order, from selecting a project topic to data collection and preprocessing, designing chunking strategies, integrating the RAG system, evaluating performance, writing result reports, and final presentations and code reviews. Based on what you learn here, developers can conduct practical projects either as a team or individually, allowing them to fully internalize the content learned in this course.

🏆 Expected Benefits

Based on the concept of cognitive load, you will clearly understand the LLM context window and token limits, and acquire strategies to manage them.
By using various chunking techniques and chunk optimization methods, you can efficiently segment 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.
You will learn how to provide stable and high-performance AI services in real-time production environments through dynamic optimization and performance evaluation.
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

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

I have experience in various forms of writing, translation, consulting, development, and teaching.

Curriculum

All

474 lectures ∙ (62hr 1min)

Course Materials:

Lecture resources

Section 1. <2nd Edition> Section 1. [Basics 1] Introduction to the Lecture and Basic Concepts

13 lectures ∙ (7hr 48min)

Section 2. <2판> Section 2. Code implementation practice for [Basic 1]

9 lectures ∙ (2hr 18min)

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

12 lectures ∙ (3hr 57min)

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

8 lectures ∙ (2hr 5min)

Section 5. <2nd edition: Course materials revised, video being uploaded> Section 5. [Basic 3] Chunking Strategy - Chunk Size and Structure

6 lectures ∙ (1hr 54min)

Section 6. <2nd edition: course materials revision, video in preparation> Section 6. Code implementation practice for [Basic 3]

2 lectures

Section 7. <1st Edition> [Basic 4] High-Quality Chunk Generation Techniques (Scheduled for deletion after completing the 2nd Edition section)

7 lectures ∙ (4hr 16min)

Section 8. <2nd Edition: Course Materials Being Revised, Video in Preparation> Section 7. [Basic 4] Techniques for Generating High-Quality Chunks

7 lectures

Section 9. <1st Edition> [Basic 5] RAG Pipeline Integration Stage 1: Preprocessing (Scheduled for deletion after completing the related 2nd Edition section)

8 lectures ∙ (2hr 8min)

Section 10. <1st edition> [Basic 6] RAG Pipeline Integration Phase 2: Retrieval (Scheduled for deletion after completing the relevant 2nd edition section)

8 lectures ∙ (2hr 13min)

Section 11. <1st Edition> [Basic 7] RAG Pipeline Integration Stage 3: Chunk Reconstruction and Prompt Redesign (To Be Deleted After Completing the Corresponding 2nd Edition Section)

8 lectures ∙ (2hr 21min)

Section 12. <1st Edition> [Basic 8] RAG Pipeline Integration Step 4: LLM Answer Generation and Post-Processing (To Be Deleted After Completing the Relevant 2nd Edition Section)

8 lectures ∙ (1hr 43min)

Section 13. <1st Edition> [Basic 9] RAG Pipeline Integration Stage 5: Hallucination Check and Reinjection Strategy (Scheduled for deletion after completing the related 2nd Edition section)

8 lectures ∙ (2hr 5min)

Section 14. <1st edition> [Basic 10] RAG Pipeline Integration Practice: Building a RAG System That Responds to Simple Questions (Scheduled for deletion after completing the related 2nd edition section)

8 lectures ∙ (2hr 52min)

Section 15. <1st Edition> [Advanced 1] Dynamic Optimization: Evaluating Question Complexity (To be deleted after completing the relevant 2nd Edition section)

8 lectures ∙ (3hr 24min)

Section 16. <1st Edition> [Advanced 2] Dynamic Optimization: Dynamic Chunk Size Adjustment Algorithm (Scheduled for deletion after completing the corresponding 2nd Edition section)

8 lectures ∙ (5hr 0min)

Section 17. <1st edition> [Advanced 3] Dynamic Optimization: Adaptive Summary Parameter Adjustment (to be deleted after completing the related 2nd edition section)

8 lectures ∙ (5hr 12min)

Section 18. <1st Edition> [Advanced 4] Dynamic Optimization: Managing Cumulative Context in Multi-turn Conversations I (To be deleted after completing the relevant 2nd Edition section)

5 lectures ∙ (3hr 8min)

Section 19. <1st Edition> [Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-Turn Conversations II (Scheduled for deletion after the related 2nd Edition section is completed)

5 lectures ∙ (54min)

Section 20. <2nd Edition> Section 29. [Advanced 4] Dynamic Optimization - Context Accumulation Management in Multi-Turn Conversations III

7 lectures ∙ (3hr 11min)

Section 21. <2nd Edition> Section 30. [Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-Turn Conversations IV

2 lectures ∙ (1hr 4min)

Section 22. <2nd Edition> Section 31. [Advanced 4] Code Implementation Practice

3 lectures

Section 23. <2nd Edition> Section 32. [Advanced 5] Dynamic Optimization: System Monitoring and Feedback Loop Design

8 lectures ∙ (3hr 31min)

Section 24. <2nd edition> Section 33. Code implementation practice for [Advanced 5]

3 lectures

Section 25. <2판> Section 34. [Advanced 6] Evaluating Performance: Measuring Recall and Accuracy

8 lectures

Section 26. <2nd Edition> Section 35. Code Implementation Practice for [Advanced 6]

4 lectures

Section 27. <2nd Edition> Section 36. [Advanced 7] Performance Evaluation: Latency Analysis and Cost Analysis

8 lectures

Section 28. <2nd Edition> Section 37. [Advanced 7] Code Implementation Practice

2 lectures

Section 29. <2nd Edition> Section 38. [Advanced 8] Performance Evaluation: Token Usage Efficiency Metrics

8 lectures

Section 30. <2nd Edition> Section 39. Code Implementation Practice for [Advanced 8]

4 lectures

Section 31. <2판> Section 40. [Advanced 9] Performance Evaluation: User Satisfaction Evaluation Methodology

8 lectures

Section 32. <2nd Edition> Section 41. Code Implementation Practice for [Advanced 9]

3 lectures

Section 33. <2nd Edition> Section 42. [Advanced 10] Performance Evaluation: Validating Strategies through A/B Testing

8 lectures

Section 34. <2nd Edition> Section 43. Code Implementation Practice for [Advanced 10]

1 lectures

Section 35. <2nd Edition> Section 44. [Advanced 11] Technology Outlook: Research Challenges in Fully Automated Chunk Optimization

8 lectures

Section 36. ==> The sections below are intended to replace or supplement the first-edition sections. <==

1 lectures

Section 37. <2nd Edition> Section 8. Practice implementing the code for [Basic 4]

5 lectures

Section 38. <2nd Edition> Section 9. [Basic 5] RAG Pipeline Integration Step 1 - Preprocessing

8 lectures

Section 39. <2nd Edition> Section 10. Practice Implementing the Code for [Basic 5]

1 lectures

Section 40. <2nd Edition> Section 11. [Basic 6] RAG Pipeline Integration Stage 2 - Retrieval (Similarity Search and Filtering)

8 lectures

Section 41. <2nd Edition> Section 12. Practice implementing the code for [Basic 6]

3 lectures

Section 42. <2nd Edition> Section 13. [Basic 7] RAG Pipeline Integration Stage 3 - Chunk Reconstruction and Prompt Redesign

8 lectures

Section 43. <2판> Section 14. Code implementation practice for [Basics 7]

3 lectures

Section 44. <2nd Edition> Section 15. [Basics 8] RAG Pipeline Integration Step 4 - LLM Answer Generation and Post-Processing

8 lectures

Section 45. <2nd Edition> Section 16. Practice Implementing the Code for [Basic 8]

3 lectures

Section 46. <2nd Edition> Section 17. [Basics 9] Five Steps to Integrate a RAG Pipeline - Checking for Hallucinations and a Reinjection Strategy

8 lectures

Section 47. <2nd Edition> Section 18. Code Implementation Practice for [Basics 9]

2 lectures

Section 48. <2nd Edition> Section 19. [Basic 10] Hands-on RAG Pipeline Integration Practice

8 lectures

Section 49. <2nd Edition> Section 20. Practice Implementing the Code for [Basics 10]

5 lectures

Section 50. <2nd Edition> Section 21. [Advanced 1] Dynamic Optimization - Question Complexity Assessment

8 lectures

Section 51. <2nd Edition> Section 22. Code Implementation Practice for [Advanced 1]

3 lectures

Section 52. <2판> Section 23. [Advanced 2] Dynamic Optimization - Dynamic Chunk Size Adjustment Algorithm

8 lectures

Section 53. <2nd Edition> Section 24. Code Implementation Practice for [Advanced 2]

3 lectures

Section 54. <2nd edition> Section 25. [Advanced 3] Dynamic Optimization - Adaptive Summary Parameter Adjustment

8 lectures

Section 55. <2nd Edition> Section 26. Code Implementation Exercise for [Advanced 3]

2 lectures

Section 56. <2nd Edition> Section 27. [Advanced 4] Dynamic Optimization - Context Accumulation Management in Multi-turn Conversations I

6 lectures ∙ (48min)

Section 57. <2nd Edition> Section 28. [Advanced 4] Dynamic Optimization - Context Accumulation Management II in Multi-turn Conversations (Repeated Conversations)

5 lectures

Section 58. <2nd Edition> Section 45. Code implementation exercises for [Advanced 11]

3 lectures

Section 59. <2nd Edition> Section 46. [Advanced 12] Technology Outlook: Long-Term Memory Integration

8 lectures

Section 60. <2nd Edition> Section 47. Code Implementation Practice for [Advanced 12]

1 lectures

Section 61. <2nd Edition> Section 48. [Advanced 13] Technology Outlook: Large-Scale Document-Based RAG Scenarios

8 lectures

Section 62. <2nd Edition> Section 49. Code Implementation Exercise for [Advanced 13]

1 lectures

Section 63. <2nd Edition> Section 50. [Advanced 14] Technology Outlook: Multimodal Information Processing

8 lectures

Section 64. <2판> Section 51. Code implementation practice for [Advanced 14]

1 lectures

Section 65. <2nd Edition> Section 52. [Project 1] Selecting a Project Topic and Writing a Plan

8 lectures

Section 66. <2nd Edition> Section 53. Code Implementation Practice for [Project 1]

3 lectures

Section 67. <2판> Section 54. [Project 2] Building a Pipeline for Data Collection and Preprocessing

8 lectures

Section 68. <2nd Edition> Section 55. Code Implementation Practice for [Project 2]

8 lectures

Section 69. <2nd Edition> Section 56. [Project 3] Designing and Implementing a Chunking Strategy

8 lectures

Section 70. <2nd edition> Section 57. Code implementation practice for [Project 3]

4 lectures

Section 71. <2nd Edition> Section 58. [Project 4] RAG System Integration and Coordination

8 lectures

Section 72. <2nd edition> Section 59. Code Implementation Practice for [Project 4]

5 lectures

Section 73. <2nd Edition> Section 60. [Project 5] Performance Evaluation and Writing an Evaluation Report

8 lectures

Section 74. <2nd Edition> Section 61. Code Implementation Practice for [Project 5]

4 lectures

Section 75. <2nd edition> Section 62. [Project 6] Final Presentation and Code Review

8 lectures

Section 76. <2판> Section 63. Code Implementation Practice for [Project 6]

4 lectures

Section 77. <2nd Edition> Section 64. Supplement to [Basic 3]

8 lectures

Section 78. <2nd Edition> Section 65. [Advanced] Harness Engineering for RAG Pipelines - Reinterpreting It as a Three-Layer Structure

8 lectures

Section 79. <2nd Edition> Section 66. [Hands-on] Implementing a RAG Harness - Verification Loop, Adaptive Pipeline, Tool Integration

7 lectures

Published: 06/19/2025

Last updated: 09/28/2026

Reviews

All

2 reviews

5.0

2 reviews

jjhgwx
Reviews 1,196
∙
Average Rating 4.9
07/12/2025
5
7% enrolled
Good lecture, thanks!
- arigaram
  Instructor
  07/12/2025
  I'm glad it was a beneficial lecture.
sojimara6279
Reviews 1
∙
Average Rating 5.0
06/05/2026
5
30% enrolled
The variety of content is great; I should check in often.
- arigaram
  Instructor
  06/05/2026
  Thank you.

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This course is not about collecting a large number of prompts. It is about learning an AI directing framework that can be applied immediately to any role or task. Even if you download prompts for specific tasks, in practice you still need to find the prompt you need, adapt it to your situation, and spend time revising the output. Through the M·A·G·I·C prompt framework, you will learn how to design the purpose, context, examples, completion criteria, and review criteria all at once. From reports, proposals, and marketing execution plans to meeting summaries, work emails, and repetitive task automation, you can connect everything with a single framework, reduce unnecessary revision conversations with AI, and build practical AI skills that will change the speed of your work starting tomorrow.

Beginner

AI, Business Productivity, ChatGPT

[MAGIC Prompt Master] AI Direction That Changes the Pace of Work

CON100

Practical Application ALL-IN-ONE Package Using ChatGPT

comento

You can learn everything from basic usage of generative AI to automation and creating customized AI chatbots for practical work applications all in one comprehensive course.

Intermediate

Chatbot, AI, ChatGPT

Practical Application ALL-IN-ONE Package Using ChatGPT

comento

Building an AI Judge with CrewAI and LLM

arigaram

Intermediate

AI, LLM, AI Agent

Building an AI Judge with CrewAI and LLM

arigaram

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

kodekorea

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

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

The qualities of engineers who thrive even in the AI era: mastering system design at major U.S. tech companies, algorithmic thinking, and open-source practice.

americasnail

A comprehensive course covering AI-era-proof Silicon Valley internships, 30 big-tech system designs, and hands-on open-source contributions. Using real-world cases from Netflix, Tesla, Meta, Tinder, and other big tech companies, you’ll design and scale 30 practical system designs. Finally, you’ll design your own service from architecture to database to scaling.

Intermediate

DBMS/RDBMS, software-design, system-design

The qualities of engineers who thrive even in the AI era: mastering system design at major U.S. tech companies, algorithmic thinking, and open-source practice.

americasnail

AX Fundamentals Training for Corporate Executives

codyjo7830

The starting point for a successful AX (AI Transformation) is not technology, but the leadership's understanding of AI. This course is designed to help executives grasp the overall flow of AI easily and clearly, while covering ways to maximize business productivity through the familiar Google Workspace and Gemini. Moving beyond mere theory, we provide 5-minute hands-on tasks for each session and a 1:1 customized online meeting (30 minutes) with AX Cody to systematically guide your company's tailored AI adoption strategy.

Beginner

AI, AI Agent, AX(Agent Experience)

AX Fundamentals Training for Corporate Executives

codyjo7830

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

AI Beginner-Intermediate 4-Week Challenge - 15 AI Tools All at Once! (Including Detailed Study Materials + Assignments)

seulkikang

Which AI tool should I learn first? Stop worrying now! - Rated 4.8 by 80 students ⭐️⭐️⭐️⭐️⭐️ - ChatGPT, Claude, Gemini, Genspark, NotebookLM, Gamma, Skywork, Manus, Antigravity, Claude Code, MiriCanvas, Opal, CapCut, and more - Learn the core features of over 15 tools very easily over 20 days. - Through weekly assignments, we provide a course you can "internalize" rather than just "passively listen to." - All-in-one coverage: Prompt Engineering > Image Generation > PPT & Video Creation > Vibe Coding - 4 hours of lecture videos + weekly study materials + assignment certification system

Beginner

gemini, AI, ChatGPT

AI Beginner-Intermediate 4-Week Challenge - 15 AI Tools All at Once! (Including Detailed Study Materials + Assignments)

seulkikang

The Complete Guide to Claude Code: From Basics to Service Deployment

Kyo

Master Claude Code from the basics to a professional level. Learn core features (CLAUDE.md · subagents · MCP · Hooks, etc.), develop and deploy a real-world service, and wrap up with foundational appendices and advanced-use lessons for non-majors.

Basic

Next.js, supabase, AI

The Complete Guide to Claude Code: From Basics to Service Deployment

Kyo

Practical Harness Engineering Completed in 2 Hours

knodark74

Building an MVP with AI is no longer difficult. However, most projects stop at the next stage. 👉 You’ve built the features 👉 But development doesn't continue Why does this happen? The problem isn't the code; 👉 It's because there is no structure that allows AI to work continuously. --- In this course, based on an existing project, 👉 We will cover the process of building a structure 👉 That allows AI to continue development on its own. --- Instead of simply using AI tools, we will: * Create a docs structure * Define the SSOT (Single Source of Truth) * Execute development on a per-ticket basis * Connect QA and iteration cycles 👉 To complete a single "AI Development System." --- Through this process, you will: 👉 Create a structure where development continues 👉 Even without a human manually coding. In other words, you can: 👉 Build and understand a development system 👉 That operates AI like a team. --- This course is designed for: 👉 Those who want to build a structure applicable to their projects immediately 👉 Those who started development with AI but found it difficult to sustain 👉 Those who want to move to the next level after "Vibe Coding" --- Beyond just learning, we provide an experience where you: 👉 Take action 👉 Build a structure that actually works 👉 And walk away with a framework you can apply to your own projects.

Basic

Python, cursor, ChatGPT

Practical Harness Engineering Completed in 2 Hours

knodark74

Building OCR that actually works in real-world scenarios, here's how to do it.

nexthumans

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

Basic

Python, AI, openai

Building OCR that actually works in real-world scenarios, here's how to do it.

nexthumans

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

multicore

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

multicore

[Author-Led Lecture] Linear Algebra for AI Development with Python

madohakja

Linear Algebra in One Go. Learn linear algebra concepts in detail—concepts that, once learned, will serve as the building blocks for your thinking for a lifetime. Going beyond simple explanations, we kindly guide you through practical exercises to show you how to apply them in real-world situations.

Beginner

Python, Pandas, Numpy

[Author-Led Lecture] Linear Algebra for AI Development with Python

madohakja

Cognitive Load Management Techniques to Break Through RAG Performance Limits (Subtitle: Mastering Context Engineering)

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. Advanced Techniques for High-Quality Chunking

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 Benefits

Recommended for
these people

Hello
This is arigaram

Curriculum

Reviews

arigaram's other courses

Similar courses

Cognitive Load Management Techniques to Break Through RAG Performance Limits (Subtitle: Mastering Context Engineering)

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. Advanced Techniques for High-Quality Chunking

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 Benefits

Recommended for these people

HelloThis is arigaram

Curriculum

Reviews

arigaram's other courses

Similar courses

Recommended for
these people

Hello
This is arigaram