[November Reimbursement Course] Practical Agentic AI Development: LLM + RAG + Tools + Workflow
- Cohort 8
- 89
- 5 · 2
A live challenge that both general individual customers and employer training customers can attend
Individual customers are not eligible for a refund. Corporate training customers, please check the application and refund process below.
- Participate in 1 of the 2 days
- Submit 2 of the 4 missions required
- Completion requires fulfilling both items 1 and 2
- If the minimum is not met, submit 3 out of 4 missions
- Proceed when the minimum number of participants, 3, is reached
- If fewer than 3 participants, the matter will be carried over or refunded after consulting with the manager
- 2 screenshots in total (directly tied to the completion criteria)
- Once · Within 30 minutes of starting
- 2 times · approximately 30 minutes before the end
- Camera must be ON when taking screenshots (with your face visible)
- Completion is not possible if your face is not visible
gov.partnership@inflab.com
Designing an AI Agent that makes decisions and takes action autonomously
It doesn’t stop at calling an LLM.
Build Agents yourself that combine RAG and Tools to get the job done.
Building an AI Agent,
are you struggling with
these challenges?
take action autonomously.
In 6 hours, you’ll have an Agent that
gets your work done.
100% Live in real time
Build it live together with the knowledge expert. Ask questions as soon as you get stuck and solve them right there.
Close Mission Support
Each session has its own mission. You’ll pass with dedicated support from the operations manager and feedback from the knowledge sharer.
A total of 6 hours, completed in 2 sessions
Two 3-hour sessions. From Agent fundamentals to workflow automation Agents, you’ll complete everything in a single course.
Building a RAG System
Real-World Project
Each lesson features a
working Agent.
A Workflow that completes tasks
from start to finish
Beyond LLM calls, design Agent workflows that complete tasks—from decision-making and branching to completion—with a single instruction.
RAG: When to Use It and When Not To
Use It
Learn the design criteria for distinguishing situations that require search from those where reasoning is sufficient, achieving both accuracy and speed.
As the Agent's tool
action
Complete an Agent that can be used immediately in practice with a ReAct structure that interprets results and determines the next action.
Knowledge Sharer Introduction
CEO of Dapada Co., Ltd., an AI and data education company
Adjunct Professor, Department of Computer Software, Induk University
Delivered numerous lectures and training sessions on LLMs/Agents for large corporations, financial institutions, and public institutions
Operating courses in the LLM field on Inflearn
Book: 『Introduction to LangChain: From RAG Chatbots to Agents』
Book 『Python Deep Learning with TensorFlow』
Book: *Introduction to Python Deep Learning and Machine Learning*
A total of 4 modules
Apply them to your work
Each module follows an "theory → practice → mission" structure.
- LLM-based AI Agent Concepts and Principles
- LangChain Basics
- Key Prompt Engineering Techniques
- Build Your Own Expert Chatbot
- Understanding RAG Architecture
- Document preprocessing and Vector Database construction
- Developing a RAG-based Q&A Agent
- Building a company document search system
- Tool/Function Calling Concepts and Implementation
- Hands-on practice implementing various tools
- Designing a ReAct Agent
- Create a Smart Assistant Agent
- Advanced Agents Built with LangGraph
- State Management · Memory Strategy
- Build a Workflow Automation Agent
A live course created and operated by
Inflearn, where countless people learn
90% of the education costs refunded
If you’re a small or medium-sized business, this course is available through government funding.
the rest is covered by government support
Guaranteed minimum for companies with fewer than 500 employees
is handled by Inflearn.
Applications must be submitted in the company’s name by the person in charge of HR or training. This is available to priority support enterprises (employees currently enrolled in employment insurance), and applications may close early once the budget is exhausted.
Now you delegate tasks to an Agent
Build an Agent structure that makes decisions and takes action on its own.
Ask questions and solve problems as soon as you get stuck.