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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.

arigaram

Cognitive Load Management Technology Breaking Through the Limits of RAG Performance

컨텍스트창관리

chunk전략설계

rag시스템구축

llm성능평가및튜닝

동적최적화

입문

초급

중급이상

챗지피티

ChatGPT

대규모 언어 모델

검색증강생성

[Basic 1] Course Introduction and Basic Concepts

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[Basic 2] Context Window and Token Limits

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[Basic 3] Chunking Strategy: Size and Structure of Chunks

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[Basic 4] High-Quality Chunking Techniques

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[Basic 5] RAG Pipeline Integration Stage 1: Preprocessing

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[Basic 6] RAG Pipeline Integration Step 2: Retrieval (Similarity Search and Filtering)

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[Basic 7] RAG Pipeline Integration Step 3: Chunk Restructuring and Prompt Redesign

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

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[Basic 9] RAG Pipeline Integration Step 5: Hallucination Check and Re-injection Strategy

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

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[Advanced 1] Dynamic Optimization: Question Complexity Evaluation

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[Advanced 2] Dynamic Optimization: Dynamic Chunk Size Adjustment Algorithm

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[Advanced 3] Dynamic Optimization: Adaptive Summary Parameter Adjustment

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[Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-turn Conversations I

[Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-turn Conversations II

(Posting in progress) [Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-turn Conversations III

(Posting schedule TBD) [Advanced 4] Dynamic Optimization: Context Accumulation Management in Multi-turn Conversations III

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[Advanced 5] Dynamic Optimization: System Monitoring and Feedback Loop Design

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[Advanced 6] Performance Evaluation: Measuring Recall and Precision

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[Advanced 7] Performance Evaluation: Latency Analysis and Cost Analysis

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[Advanced 8] Performance Evaluation: Token Usage Efficiency Metrics

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[Advanced 9] Performance Evaluation: User Satisfaction Evaluation Methodology

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

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[Advanced 11] Technology Outlook: Research Challenges for Fully Automated Chunk Optimization

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[Advanced 12] Technology Outlook: Long-term Memory Integration

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[Advanced 13] Technology Outlook: Large-scale Document-based RAG Scenarios

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[Advanced 14] Technology Outlook: Multimodal Information Processing

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[Project 1] Project Topic Selection and Proposal Writing

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

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[Project 3] Chunking Strategy Design and Implementation

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[Project 4] RAG System Integration and Orchestration

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[Project 5] Performance Evaluation and Evaluation Report Writing

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[Project 6] Final Presentation and Code Review

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(Posting schedule TBD) Supplement to [Basic 3]