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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

Generative AI

[Basic 1] Course Introduction and Fundamental Concepts

(Scheduled: Date TBD) Code implementation practice for [Basic 1]

[Basic 2] Context Window and Token Limitations

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[Basic 3] Chunking Strategy: Chunk Size and Structure

(Scheduled: TBD) Code implementation practice for [Basic 3]

[Basic 4] High-Quality Chunk Generation Techniques

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[Basic 5] RAG Pipeline Integration Step 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 Reconstruction 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

[Advanced 1] Dynamic Optimization: Question Complexity Assessment

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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

(Scheduled: 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 Assessment Methodology

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

Practice implementing code for [Advanced 10]

[Advanced 11] Technical Outlook: Fully Automated Chunk Optimization Research Task

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

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[Advanced 13] Technical 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 Coordination

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[Project 5] Performance Evaluation and Assessment Report Writing

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[Project 6] Final Presentation and Code Review

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(Scheduled: Date TBD) Supplement for [Basic 3]