RAG SYSTEM
RAG System Master
Connect retrieval and generation through GraphRAG and Agentic RAG to understand the full RAG system architecture.
Enrollment and payment take place on Inflearn.
- Listed entries
- 29 entries
- Video duration
- 2h 54m
- Provider update
- Course audio
- Korean
Check whether this course fits
- Who it is for
- For developers improving RAG retrieval and architecture and integrating LLMs into services.
- Before you start
- Basic RAG and prompting concepts, Python LLM API integration experience, and basic vector database skills.
- What you can do
- Understand when to use Classic, Graph, and Agentic RAG
- Design for retrieval quality, token cost, and latency
Practice in the public curriculum
Put the topic into practice
Compare retrieval designs and connect routing and memory while assessing quality, cost, and latency.
- Python
- LLM API
- Vector DB
- GraphRAG
Tool subscriptions and API usage may cost extra. Check the current terms for the tools you plan to use.
Curriculum overview
- Retrieval architecture comparison
- Search optimization and routing
- Knowledge graphs and self-evaluation
- Long-term memory and hybrid systems
Entry counts include materials and guidance. This overview groups the learning topics.
See the full curriculum and previews ↗Before you enroll
Where can I check price, access, and support?
Use the current Inflearn listing. Discounts, access duration, and question support vary by course.
Where should I start if I lack prerequisites?
Check the prerequisites and choose a starting point that matches your goal and experience. The free foundations course introduces the big picture of AI agents.
Find my starting coursePublic information checked: 2026-10-07 · Inflearn source ↗