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Mastering RAG Systems: Perfect Design from Classic to Agentic

Learn the core principles and practical implementation of Classic RAG, Graph RAG, and Agentic RAG. You will design dynamic routing systems that optimize token efficiency and latency, and build long-term memory systems that combine GraphRAG's relational network reasoning with Agentic RAG's self-evaluation loops. This course is designed to complete your expertise in designing advanced RAG architectures that can be immediately applied in professional practice.

11 learners are taking this course

Level Intermediate

Course period Unlimited

RAG
RAG
AI Agent
AI Agent
AI
AI
React
React
RAG
RAG
AI Agent
AI Agent
AI
AI
React
React

What you will gain after the course

  • Architectural differences between Classic, Graph, and Agentic RAG and determining optimal use cases

  • Implementing a dynamic routing system considering token costs and latency

  • Deploying a long-term memory system combining GraphRAG and xMemory


Is your RAG still inefficient?
[AI Agent]

This course will help you develop practical skills to clearly distinguish the architectural differences and optimal use cases for Classic RAG, Graph RAG, and Agentic RAG, implement a dynamic routing system that considers token costs and latency, and deploy a long-term memory system combining GraphRAG and xMemory.


Have you felt the limitations of a single architecture while experiencing the high latency, token costs, and accuracy issues of existing RAG architectures?

Have you realized that you need to dynamically select the optimal pipeline that fits numerous query characteristics?

Do you want to move beyond uniform RAG designs and personally design a hybrid routing system that selects the optimal pipeline based on query characteristics?

Complete your vague RAG system designs with clear principles and practical implementation.
Through this course, you will gain a deep understanding of various RAG architectures and acquire the expertise to apply them to real-world problem-solving.


Systematically learn everything from the core principles of Classic, Graph, and Agentic RAG architectures
to their practical application methods.

Go beyond simple RAG implementation and design dynamic routing and
long-term memory systems to become an AI Agent Expert.

By the end of this course, you will


Beyond simply understanding the complexity of RAG architecture, you will be able to design and optimize it yourself.

  • You will gain a clear understanding of the core principles, strengths, and weaknesses of Classic RAG, Graph RAG, and Agentic RAG. You will develop the insight to determine which RAG approach is most effective based on the characteristics of a query and design the optimal architecture. You will no longer feel uncertain when designing RAG systems.


Advanced RAG

✔️

RAG systems are no longer an option, but a necessity! Put an end to your complex design worries.

RAG System Master:
Perfect Design from Classic to Agentic

This course systematically covers everything from the core principles to the practical implementation of Classic RAG, Graph RAG, and Agentic RAG. By combining dynamic routing system design to optimize token efficiency and latency with advanced techniques in GraphRAG and Agentic RAG, you will complete your skills in designing RAG architectures that can be immediately applied in real-world practice.

Evolutionary Trends of RAG

Practical Application: Building Advanced RAG Based on Long-Term Memory Systems (xMemory)

You will learn how to build a long-term memory system by following the code, which extracts knowledge graphs from text and combines relationship network reasoning with Agentic RAG's self-evaluation loops. Through this, you will gain experience in directly designing and deploying complex knowledge management and retrieval systems.

Architecture design code, from Classic to Agentic RAG

We provide the essential code and examples needed to clearly understand the differences between Classic, Graph, and Agentic RAG covered in the lecture, and to determine the optimal use-case scenarios for each architecture. This allows you to focus on directly designing and implementing advanced RAG architectures.


We can solve the concerns of
these people!

📌
Those who are repeating similar trial and error every time due to a lack of deep understanding of RAG architecture design
when building complex knowledge management systems

📌

AI Product Manager
Those who want to acquire the latest RAG technology trends and practical architecture design capabilities
to successfully launch production-level LLM applications


📌

AI Engineers
Those who are struggling with performance optimization due to high latency
and token cost issues in existing RAG systems




Prerequisites and Important Notes

  • Basic knowledge of Python programming is required

  • It will be even more beneficial if you have experience building RAG systems.


RAG, AI Agent, Artificial Intelligence (AI), React

Recommended for
these people

Who is this course right for?

  • AI engineers who need to optimize the performance of RAG systems

  • A backend developer designing a complex knowledge management system

  • AI Product Manager looking to build production-level LLM applications

Need to know before starting?

  • Understanding the Basic Concepts of Prompt Engineering and RAG

  • Experience in Python-based LLM API integration

  • Basic usage of vector databases (Pinecone, Weaviate, etc.)

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YouTube: https://youtube.com/channel/UChmHjzyYedu9yYb3YmnOOog?si=xM1HueA3TJ4BjnV3

Contact: codebridge747@gmail.com

Experience

Developer at a major IT corporation in South Korea

Bachelor's degree in Computer Engineering

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Curriculum

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19 lectures ∙ (1hr 54min)

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